{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "***\n",
    "***\n",
    "# Machine Learning for Social Scientists\n",
    "\n",
    "***\n",
    "***\n",
    "\n",
    "Cheng-Jun Wang \n",
    "\n",
    "wangchengjun@nju.edu.cn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Computational Social Science\n",
    "    - Lazer,et al. 2009; Watts, 2007\n",
    "- Aritifical Intelligence\n",
    "    - Machine Learning, e.g., Neural Networks\n",
    "- Data Science\n",
    "    - The center of Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T16:09:51.234644Z",
     "start_time": "2019-06-17T16:09:51.229465Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "> “Know thyself.” ― Socrates\n",
    "\n",
    "> “The unexamined life is not worth living.” ― Socrates \n",
    "\n",
    "> “Wonder is the beginning of wisdom.” ― Socrates\n",
    "\n",
    ">  “True wisdom comes to each of us when we realize how little we understand about life, ourselves, and the world around us.” \n",
    "― Socrates\n",
    "\n",
    "> “Education is the kindling of a flame, not the filling of a vessel.” ― Socrates\n",
    "\n",
    "> “Strong minds discuss ideas, average minds discuss events, weak minds discuss people.” \n",
    "― Socrates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "1. **机器学习简介**：从泰坦尼克号讲起\n",
    "\n",
    "> 本部分介绍基于python进行机器学习的基本逻辑(需要学员提前安装anaconda)、Scikit-Learn、机器学习模型的参数和模型校验、特征提取。\n",
    "\n",
    "2. **机器学习初步**: 朴素贝叶斯与线性回归\n",
    "3. **机器学习进阶**: 支持向量机与随机森林\n",
    "4. **机器学习扩展**: 基于Pytorch的神经网络模型\n",
    "\n",
    "https://github.com/computational-class/machine-learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "***\n",
    "***\n",
    "# 机器学习简介：\n",
    "\n",
    "###  从泰坦尼克号讲起\n",
    "\n",
    "***\n",
    "***\n",
    "\n",
    "王成军 \n",
    "\n",
    "wangchengjun@nju.edu.cn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "<img src = './img/machine2.jpg' width = 800>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 1、 监督式学习\n",
    "\n",
    "工作机制：\n",
    "- 这个算法由一个目标变量或结果变量（或因变量）组成。\n",
    "- 这些变量由已知的一系列预示变量（自变量）预测而来。\n",
    "- 利用这一系列变量，我们生成一个将输入值映射到期望输出值的函数。\n",
    "- 这个训练过程会一直持续，直到模型在训练数据上获得期望的精确度。\n",
    "- 监督式学习的例子有：回归、决策树、随机森林、K – 近邻算法、逻辑回归等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 2、非监督式学习\n",
    "\n",
    "工作机制：\n",
    "- 在这个算法中，没有任何目标变量或结果变量要预测或估计。\n",
    "- 这个算法用在不同的组内聚类分析。\n",
    "- 这种分析方式被广泛地用来细分客户，根据干预的方式分为不同的用户组。\n",
    "- 非监督式学习的例子有：关联算法和 K–均值算法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 3、强化学习\n",
    "\n",
    "工作机制：\n",
    "- 这个算法训练机器进行决策。\n",
    "- 它是这样工作的：机器被放在一个能让它通过反复试错来训练自己的环境中。\n",
    "- 机器从过去的经验中进行学习，并且尝试利用了解最透彻的知识作出精确的商业判断。 \n",
    "- 强化学习的例子有马尔可夫决策过程。alphago\n",
    "\n",
    "> Chess. Here, the agent decides upon a series of moves depending on the state of the board (the environment), and the\n",
    "reward can be defined as win or lose at the end of the game:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src = './img/mlprocess.png' width = 800>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "---\n",
    "\n",
    "# 泰坦尼克号数据分析\n",
    "---\n",
    "王成军\n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T12:14:27.789819Z",
     "start_time": "2019-06-16T12:14:27.785423Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src = './img/titanic.jpg' width = 800>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T12:21:44.813053Z",
     "start_time": "2019-06-16T12:21:44.808742Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src = './img/titanic3.jpg' width = 800>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- 英国白星航运公司邮轮，排水量46000吨，1912年4月2日试航。当时世界上体积最庞大、有“永不沉没”的美誉 。\n",
    "- 处女航，从英国南安普敦出发，驶向美国纽约。\n",
    "- 1912年4月14日23时40分左右，泰坦尼克号与一座冰山相撞\n",
    "- 次日凌晨2时20分左右，裂成两截后沉入大西洋底3700米处。\n",
    "- 2224人，逾1500人丧生，仅333具罹难者遗体被寻回。\n",
    "- 和平时期死伤人数最为惨重的一次海难；\n",
    "- 其残骸直至1985年才被再度发现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:52:54.915487Z",
     "start_time": "2019-06-19T01:52:54.894886Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 12))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "# https://www.kaggle.com/c/titanic\n",
    "train = pd.read_csv('./data/titanic_train.csv',  sep = \",\")\n",
    "test  = pd.read_csv('./data/titanic_test.csv',   sep = \",\")\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:53:23.251480Z",
     "start_time": "2019-06-19T01:53:23.234326Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "| **Variable** | **Definition**                             | **Key**                                        |\n",
    "| :----------- | :----------------------------------------- | :--------------------------------------------- |\n",
    "| survival     | Survival                                   | 0 = No, 1 = Yes                                |\n",
    "| pclass       | Ticket class                               | 1 = 1st, 2 = 2nd, 3 = 3rd                      |\n",
    "| sex          | Sex                                        |                                                |\n",
    "| Age          | Age in years                               |                                                |\n",
    "| sibsp        | # of siblings / spouses aboard the Titanic |                                                |\n",
    "| parch        | # of parents / children aboard the Titanic |                                                |\n",
    "| ticket       | Ticket number                              |                                                |\n",
    "| fare         | Passenger fare                             |                                                |\n",
    "| cabin        | Cabin number                               |                                                |\n",
    "| embarked     | Port of Embarkation                        | C = Cherbourg, Q = Queenstown, S = Southampton |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Variable Notes\n",
    "\n",
    "**pclass**: A proxy for socio-economic status (SES)\n",
    "1st = Upper\n",
    "2nd = Middle\n",
    "3rd = Lower\n",
    "\n",
    "**age**: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "**sibsp**: The dataset defines family relations in this way...\n",
    "Sibling = brother, sister, stepbrother, stepsister\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "**parch**: The dataset defines family relations in this way...\n",
    "Parent = mother, father\n",
    "Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Cleaning or Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:55:29.815302Z",
     "start_time": "2019-06-19T01:55:29.806697Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# dealing with missing data\n",
    "train[\"Age\"] = train[\"Age\"].fillna(train[\"Age\"].median())\n",
    "train[\"Fare\"] = train[\"Fare\"].fillna(train[\"Fare\"].median())\n",
    "# Impute the Embarked variable\n",
    "train[\"Embarked\"] = train[\"Embarked\"].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:55:57.634771Z",
     "start_time": "2019-06-19T01:55:57.623701Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Convert the male and female groups to integer form\n",
    "train[\"Sex\"][train[\"Sex\"] == \"male\"] = 0\n",
    "train[\"Sex\"][train[\"Sex\"] == \"female\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:56:03.599151Z",
     "start_time": "2019-06-19T01:56:03.594440Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "train['Sex'] = train['Sex'].fillna('ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T01:56:15.515363Z",
     "start_time": "2019-06-19T01:56:15.498521Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Convert the Embarked classes to integer form\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"S\"] = 0\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"C\"] = 1\n",
    "train[\"Embarked\"][train[\"Embarked\"] == \"Q\"] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Describing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.369872Z",
     "start_time": "2019-06-17T07:22:35.334524Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.361582    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.019697    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.378561Z",
     "start_time": "2019-06-17T07:22:35.371883Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passengers that survived vs passengers that passed away\n",
    "train[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.483136Z",
     "start_time": "2019-06-17T07:22:35.380605Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As proportions\n",
    "train[\"Survived\"].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.492908Z",
     "start_time": "2019-06-17T07:22:35.485892Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    577\n",
       "1    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.514268Z",
     "start_time": "2019-06-17T07:22:35.495750Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name Sex   Age  SibSp  Parch  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...   1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina   1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   1  35.0      1      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "1          PC 17599  71.2833   C85        1  \n",
       "2  STON/O2. 3101282   7.9250   NaN        0  \n",
       "3            113803  53.1000  C123        0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleting the dataset of females\n",
    "train[train['Sex']==1][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.529314Z",
     "start_time": "2019-06-17T07:22:35.517417Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived    Fare\n",
       "0         0  7.2500\n",
       "4         0  8.0500\n",
       "5         0  8.4583"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Males that survived vs males that passed away\n",
    "train[[\"Survived\", 'Fare']][train[\"Sex\"] == 0][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.541869Z",
     "start_time": "2019-06-17T07:22:35.531819Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    468\n",
       "1    109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Males that survived vs males that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 0].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.553574Z",
     "start_time": "2019-06-17T07:22:35.543932Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    233\n",
       "0     81\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Females that survived vs Females that passed away\n",
    "train[\"Survived\"][train[\"Sex\"] == 1].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.565113Z",
     "start_time": "2019-06-17T07:22:35.555665Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.811092\n",
       "1    0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized male survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 0].value_counts(normalize = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.576522Z",
     "start_time": "2019-06-17T07:22:35.567038Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.742038\n",
       "0    0.257962\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized female survival\n",
    "train[\"Survived\"][train[\"Sex\"] == 1].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.589286Z",
     "start_time": "2019-06-17T07:22:35.578069Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Create the column Child, and indicate whether child or not a child. Print the new column.\n",
    "train[\"Child\"] = float('NaN')\n",
    "train.Child[train.Age <=18] = 1\n",
    "train.Child[train.Age > 18] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.599891Z",
     "start_time": "2019-06-17T07:22:35.591097Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.503597\n",
       "0    0.496403\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized Survival Rates for under 18\n",
    "train.Survived[train.Child == 1].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.619002Z",
     "start_time": "2019-06-17T07:22:35.601718Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.704545\n",
       "0    0.295455\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the column Child, and indicate whether child or not a child. Print the new column.\n",
    "train[\"Child\"] = float('NaN')\n",
    "train.Child[train.Age <=5] = 1\n",
    "train.Child[train.Age > 5] = 0\n",
    "# Normalized Survival Rates for under 18\n",
    "train.Survived[train.Child == 1].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.639999Z",
     "start_time": "2019-06-17T07:22:35.620766Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass         1         2         3\n",
       "Sex                                 \n",
       "0       0.368852  0.157407  0.135447\n",
       "1       0.968085  0.921053  0.500000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.pivot_table('Survived', ['Sex'], 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.674606Z",
     "start_time": "2019-06-17T07:22:35.642060Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.350427</td>\n",
       "      <td>0.086022</td>\n",
       "      <td>0.121622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.975904</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.495050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass               1         2         3\n",
       "Sex Age                                   \n",
       "0   (0, 18]   0.800000  0.600000  0.215686\n",
       "    (18, 80]  0.350427  0.086022  0.121622\n",
       "1   (0, 18]   0.909091  1.000000  0.511628\n",
       "    (18, 80]  0.975904  0.903226  0.495050"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(train['Age'], [0, 18, 80])\n",
    "train.pivot_table('Survived', ['Sex', age], 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.686627Z",
     "start_time": "2019-06-17T07:22:35.677683Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:35.745542Z",
     "start_time": "2019-06-17T07:22:35.688390Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.106122</td>\n",
       "      <td>0.369369</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.196078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.884615</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.975904</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.439024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fare         (-0.001, 14.454]                     (14.454, 512.329]            \\\n",
       "Pclass                      1         2         3                 1         2   \n",
       "Sex Age                                                                         \n",
       "0   (0, 18]               NaN  0.000000  0.260870          0.800000  0.818182   \n",
       "    (18, 80]              0.0  0.118644  0.106122          0.369369  0.029412   \n",
       "1   (0, 18]               NaN  1.000000  0.714286          0.909091  1.000000   \n",
       "    (18, 80]              NaN  0.884615  0.533333          0.975904  0.916667   \n",
       "\n",
       "Fare                    \n",
       "Pclass               3  \n",
       "Sex Age                 \n",
       "0   (0, 18]   0.178571  \n",
       "    (18, 80]  0.196078  \n",
       "1   (0, 18]   0.318182  \n",
       "    (18, 80]  0.439024  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(train['Fare'], 2)\n",
    "train.pivot_table('Survived', ['Sex', age], [fare, 'Pclass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T02:07:27.573461Z",
     "start_time": "2019-06-19T02:07:27.568762Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T02:07:35.895495Z",
     "start_time": "2019-06-19T02:07:35.681121Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Survived'].tolist())\n",
    "plt.xlabel('Survived', fontsize = 20)\n",
    "plt.ylabel('Frequency', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T02:07:45.223342Z",
     "start_time": "2019-06-19T02:07:45.011163Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEaCAYAAADDgSq4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAHlBJREFUeJzt3XuYXFWZ7/FvOhBMSFSIrYKOioJvEBEVBbyAcBAd5eINMzoiqKCMI86ZM4A3MAEVCV4YQcUrEkRxBIwwOAjoURgUUO7gAV6DgAxKNAaUhJAASZ8/1m4tOreq6tpVXd3fz/P0U6m91669Fh3ql7XX2mtPGhoaQpKkOgz0ugKSpPHLkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNVmo15XICK2AL4E7AE8CHw1M+dExBTgJGB2VfR04MjMXFUdtz9wPLAlcDVwSGYu7Hb9JUnr1vOQAc6jhMQTKYFxaUTcAjwP2A7YBpgGnA8cAZwQEdsB84HXAL8A5gDnRsT2mbm6iXNuArwYuAdY1dHWSNL4NRnYArgKWNnMAZN6uUBmROwMXAg8KTMfqrY9k9KjuYbSO7mg2r4/8MnMfHZEHA88KzNnV/smA0uA12bm5U2c+uXAZR1vkCRNDLsCP2umYK97MjsCNwEfi4gDgRXAF4FTKWl5c0PZW4Gtq8to2wLXD+/IzFURcRul59NMyNwDcN99D7B6deshO3PmdJYsWdbycf3MNk8Mtnn8G017BwYmsdlmm0L1HdqMXofM5sBLgZ8CWwGzgIuAxdX+5Q1llwOTKJfOpo/YN7x/0ybPuwoY/o/Vlpkzp7d9bL+yzRODbR7/OtDepocZeh0yK4EHgGMycwi4ISJOAw6q9k9tKDutel1WHdO4b3h/S/G8ZMmytnoyg4MzWLx4acvH9TPbPDHY5vFvNO0dGJjUckD1egrzrVUdGsNuI+A+YBGlZzNsFrAwMx+hXEb7675qTGZrHn15TZLUY73uyfwI+DNwXEQcTRlreSdwKHA7MDcirgM2Bo6iTGMGOBO4IiL2Ai6lzC5bRJlpJkkaI3rak8nMFcArKAP29wAXAPMy83uU4LgWuBG4AbgEmFcddxNwIHAyZVbZrsB+w/fQSJLGhl73ZMjM24G917J9BXBY9bO24xYAC+qtnSRpNHo9JiNJGscMGUlSbXp+uawfPfTwKgYHZ3T9vCtWPsLS+x/s+nklqV2GTBumbDyZfQ8/r+vnPf+zr2PizOaXNB54uUySVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVJuNel2BiHg3cAqwsmHz+4DvACcBs6ttpwNHZuaq6rj9geOBLYGrgUMyc2G36i1J2rCx0JPZETghM6c3/JwOHAtsB2wD7ADsARwBEBHbAfOBdwGbAZcB50bEWGiPJKkyFr6UdwSuX8v2g4B5mXlvZt4NHAccXO07ALggMy/LzIeAucBTgF26UWFJUnN6erksIjYGtgfeGREnA8uBrwNfAbYAbm4ofiuwdURMAbalIZgyc1VE3Ebp+VzepepLkjag12MyTwR+CZwGvBF4DnAesHG1f3lD2eXAJGAaMH3EvuH9m7Zy8pkzp7de4x4bHJwxIc/dK7Z5Yphobe5me3saMpn5O2C3hk3XVT2at1fvpzbsm1a9LgMeGLFveP+yVs6/ZMkyVq8eauUQoLd/IRcvXtqT8w4OzujZuXvFNk8ME63No2nvwMCklv9x3tMxmYjYPiLmjtj8GOAeYBEwq2H7LGBhZj5CuYz2130RMRnYmkdfXpMk9VivL5ctBT4UEXdRpijvCLwfOAx4MTA3Iq6jXD47qioDcCZwRUTsBVwKzKGE0i+6W31J0vr0tCeTmXcCr6OEyv3A2cCxmXk2JTiuBW4EbgAuAeZVx90EHAicDCwBdgX2G76HRpI0NvS6J0NmXgxcvJbtKyjhc9g6jlsALKi3dpKk0RgL98lIksYpQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVBtDRpJUG0NGklQbQ0aSVJuWQyYinlVHRSRJ489GbRyzMCIuB84AzsrM+zpcJ0nSONHO5bL5wHOBLwH3RMT3IuL1EbFxR2smSep7LYdMZr4LeBLwD8BFwD7A9yiB88WIeElnqyhJ6lftXC4jM1cCZwNnR8RmlMCZDbwb+KeIuJ1yOe1bmXl7pyorSeovo55dlpn3ZeaXgVcDbwPuAp4FHEMZv/lRRLxstOeRJPWftnoywyJigBIubwVeB0wHHqZcPjsLeAHwLuDSiHhPZn5jHZ+zKXAN8PXM/ExEPA74GvAqYAXwucyc11D+fcCHgM2AnwDvzsw/jKYtkqTOaytkIuLllGDZH3gCMAm4Evgm8B+Z+eeq6NkRcQpwE3AssNaQAU4Gtml4/+XqdUvg6cCFEXFHZn43Il4NzAH2BG4HTqFMRnhNO22RJNWn5ZCJiDuBv6MEy2+BTwLfzMyFayufmXdHxG8oYbG2z3sz8Gzg59X7TSnhtUNmLgduiYjPAwcD3wXeAZyemb+qyh8B/DEinpKZv2u1PZKk+rTTk9kcOJ0SLJc0ecyJwN0jN0bE04BPA7tTeiNQejQDQDYUvRX4P9Wft6XMagMgM/8UEfcCzwEMGUkaQ9oJmSdl5oMAETE5M1cN74iIp2fmb0cekJlnjtwWEZOBbwFHZ+adETG8azqwsvFzgeXApg37l4/4uMb9TZs5c3qrh/Tc4OCMCXnuXrHNE8NEa3M329tyyGTmgxGxJ/BZ4DTgJICImATcGhG3Ae/IzGs28FFHAb/LzG+N2P4AMCUiBjJzdbVtGrCsYf/UEcc07m/akiXLWL16qNXDevoXcvHipT057+DgjJ6du1ds88Qw0do8mvYODExq+R/n7YzJ7Ar8EFjJo3sUm1B6Jm8GfhYRu2XmVev5qH8EtoyI4UkC04GdKZe9hiiXzYYvmc0Cbq7+fHP1frg+g8DMhv2SpDGinctlc4D7gZ0z8zfDGzNzBfDuiJgHXA18jPXM+MrMWY3vI+IS4AfVFObpwLyIOBB4KvB+4CNV0TOA+RFxNmWs5tPAjzPz9220RZJUo3Zuxnw+cGZjwDSqtv8HMJobMA8FHgTuoNwH8+XM/Hb1+RcAc4FzgEWUKdQHjOJckqSatNOT2Qh4zAbKDFGmODctM3dv+PN9lMtp6yr7JcoCnZKkMaydnsz1wL7VWMgaImJzYF/gxtFUTJLU/9rpyXwO+D7w04j4OOVO//uBGcBOlFljW/K3+1okSRNUO1OYz4uIoyjLxKxx/wvlUtnczDxntJWTJPW3dpf6Pz4izqEs7/88ykKVyyhrlJ2Zmbm+4yVJE0PbqzBXa5Ud18G6SJLGmbZDJiKmUe5h2YR1zCTLTAf/JWkCa+eO/6nAVymXyjZ0/OR2KiVJGh/a6cl8jPIEzMXA5cBfKIP9kiQ9Sjsh8xbgNuBFmXl/h+sjSRpH2rkZ8wnAAgNGkrQh7YTMQsqTMSVJWq92QuZzwJsiYqdOV0aSNL60MybzCOWmy59FxKXArynPlhlpKDMPH03lJEn9rZ2Qmd/w5z2rn7UZAgwZSZrA2gmZPTpeC0nSuNTOApmX1lERSdL4M5plZTYC9qI8KXOzzPxARGwPLM3MOztUP0lSH2tndhkRsTtwO/ADyiKZw2Mvs4GFEXFER2onSeprLYdMRDwfuACYBnwS+F7D7iuBRcAJEbFvR2ooSepb7fRkjgVWADtm5keBXw3vyMz/ojwd817g3zpSQ0lS32onZHYFzsrM365tZ2beA5wFPHc0FZMk9b92QuYxwAMbKPMIMLWNz5YkjSPthMwtwF4RsdZjI2Jj4NWAj2CWpAmunZD5GuVS2PyImNm4IyKeCHwb2AY4bfTVkyT1s3ZuxvxyRLwUOIDy8LIVABFxJ+VxzAPAucAXO1ZLSVJfautmzMw8MCLOBw4GXghsDDwW+BkwPzPnN/tZEbEPZSr0VsAfgE9n5lciYgpwEuXeG4DTgSMzc1V13P7A8cCWwNXAIZm5sJ32SFK3zXjsVB6zSdv3w7ftoYdXdfV8bbcwM88Gzh7NySPiacA5wH6ZeXFE7ABcGRHXAm8EtqNcepsGnA8cQbkHZzvKQp2vAX4BzAHOjYjtM3P1aOokSd3wmE02Yt/Dz+v6ec//7Ou6er627vjvlMy8CxisAmYAmEmZmXY/cBAwLzPvzcy7KSsLHFwdegBwQWZelpkPAXOBpwC7dL0RkqR1arknU/UymjGUmTtuqFBmLo2Ix1Ju4JxMuQT2B2AL4OaGorcCW1eX0bYFrm/4jFURcRul53N5k/WTJNWsnctlz2+izF3AfS185gOUS2I7AD8EllfblzeUWQ5MqspNH7FveP+mLZxTklSzdmaXrev+mKnAs4CjKUvL7N3CZ64CVgFXRcSpwM7VrsYbOqdVr8sooTTyZs9p1b6mzZw5vZXiY8Lg4IwJee5esc0Tw0Rrczfb27GpDZn5IPCriHgrcC3wKcrYyTpFxJ7AcZnZOJYyhdILWgTMAoaXr5kFLMzMRyLi5ur98OdMBrbm0ZfXNmjJkmWsXj3UyiFAb/9CLl68tCfnHRyc0bNz94ptnhh61eZ+/B4ZGJjU8j/OOz5/LjOHIuJi4JAmil8HPCsi/oVyX81LgHcAr6eEzNyIuI4yRfooyjRmgDOBKyJiL+BSyuyyRZSZZpKkMaKu2WXPpPRI1isz76VMQ34LZeD/S8DB1dM351B6RDcCNwCXAPOq424CDgROBpZQFu3cb/geGknS2NDO7LLnrWPXAGXgfR/gDcD/bebzMvNq4KVr2b4COKz6WdtxC4AFzZxDktQb7Vwuux5Y30DGJMrA/IfbqpEkadxoJ2S+ydpDZgh4iHI/y5mZ+cfRVEyS1P/amcL8jhrqIUkah3q6rIwkaXxrZ+D/J22eaygz92zzWElSH2pnTOaZlLvrn1C9f5By8+RMYJNq2xBlAkCj1u96lCT1tXYul+1KWSX5SuBlwPTMfGpmTqWsPXYhJXR2AjZr+Nm8IzWWJPWNdnoyJ1GW498jM1c27sjMmyLidZSHiB2Tmft0oI6SpD7VTk/mlcB5IwNmWGY+TLkRc/dR1EuSNA60EzIPAs/eQJntKZfUJEkTWDshcxGwX0S8Y+SOiJgcER8G/hejfDSzJKn/tTMmcxSwF3BqRBwBXEV5jssWwIuApwG/Aj7aqUpKkvpTyz2ZzPwfysyxs4CtgIOA9wFvpMwiOxl4WWZ6uUySJri2nidTBc1bI+IxlKdhPo4ybfnXLrcvSRo2qmVlquX47wX+kpm3sOYNmJKkCaytnkxETAXmUp5iOUi5m38j4PCIeDXw3szMTlVSktSfWu7JRMR04DLgA8BK4Hb+1oOZRrk/5rKIeEZnqihJ6lftXC47Gngh8C/AM4BvD+/IzLmUiQCb4+wySZrw2gmZ2cCFmfmFzBxixMKXmXkG8ANgjw7UT5LUx9oJmS0pj2Ben6TcNyNJmsDaCZnFwLYbKPPcqpwkaQJrJ2TOB/aNiL9f286IeBPwGuCC0VRMktT/2pnCfCywL/CDiLiA8rAyIuIYyrIyrwH+CHy8Q3WUJPWpdpaV+QPlYWUXAXsDL6FMYZ4DvJYyvXm3zPxdB+spSepDLfdkImJaZt4F7B0RT6ZMZ348ZZHMGzPzzs5WUZLUr9q5XHZNRFySme/NzEU49iJJWod2QmYryuB/R0TEi4ET+duDzr5BGffZmPKo59lV0dOBI4cX4IyI/YHjKVOqrwYOycyFnaqXJGn02plddgNlgH/UqiVq/gv4LmUCwZ7AgcB7KUGzHbANsAPl5s4jquO2A+YD76I8XuAy4NyIGNWCn5KkzmqnJ/Nh4NsRcSVwLnAH5ZHMa8jM/9zAZz0N+HlmfqF6vzAivg+8nLIG2iGZeS9wb0QcB3wSOAE4ALggMy8DiIi5wGHALsDlbbRJklSDdkLmx9Xrk4AXr6PMJMpyM5PX90GZeTPwhuH3ETGFMgV6PvBW4OaG4rcCW1dltqVh1YHMXBURt1F6PoaMJI0R7YTMxxixXlknRMQmwHcoKzt/l9JjWd5QZDklvKYB00fsG96/aafrJUlq3wZDJiL2A27NzF8DZOYxna5ENRX6e9XbVwKrqz9PbSg2rXpdBjwwYt/w/mWtnHfmzOmtVXQMGBycMSHP3Su2eWKYaG3uZnub6cl8nzII/7HGjRHxNOAZmfnfo6lARDwXuJByGe7QzFxZbV8EzAJ+WxWdBSzMzEci4ubq/fBnTAa25tGX1zZoyZJlrF7deqesl38hFy9e2pPzDg7O6Nm5e8U2Twy9anM/fo8MDExq+R/nzYTMuh6p/E7KXf7rHXdZn4iYCVwMfCszPzBi9xnA3Ii4jjKd+SjKNGaAM4ErImIv4NKqHouAX7RbF0lS57X1+OUOOojySID3RcQ/N2z/IfB24DPAjZR6ngHMA8jMmyLiQOBk4KnAtcB+w/fQSJLGhp6GTGaeSLkRc10Oq37WduwCYEEd9ZIkdYY3L0qSamPISJJqY8hIkmrT7JjM7hGxxjaAiPgoa5+BNpSZPrhMkiawpkOm+lmbY9exfQifjilJE1ozIfPO2mshSRqXNhgymXn6hspIkrQ2DvxLkmpjyEiSamPISJJqY8hIkmpjyEiSamPISJJqY8hIkmpjyEiSamPISJJqY8hIkmpjyEiSamPISJJqY8hIkmpjyEiSamPISJJqY8hIkmpjyEiSamPISJJqY8hIkmqzUa8rMCwidgIuzMzNq/dTgJOA2VWR04EjM3NVtX9/4HhgS+Bq4JDMXNj1ikuS1mlM9GQiYjZwMTClYfOxwHbANsAOwB7AEVX57YD5wLuAzYDLgHMjYky0R5JU9PxLOSI+AXwQ+PiIXQcB8zLz3sy8GzgOOLjadwBwQWZelpkPAXOBpwC7dKnakqQm9DxkgFMyc0fgmuENEfF4YAvg5oZytwJbV5fRtm3cV11Cu43S85EkjRE9H5PJzN+vZfP06nV5w7blwCRgWrV/+YhjlgObtnLumTOnb7jQGDM4OGNCnrtXbPPEMNHa3M329jxk1uGB6nVqw7Zp1euyav9UHm1ata9pS5YsY/XqoZYr18u/kIsXL+3JeQcHZ/Ts3L1imyeGXrW5H79HBgYmtfyP87FwuWwNmXkfsAiY1bB5FrAwMx+hXCr7676ImAxszaMvr0mSemys9mQAzgDmRsR1wMbAUZRpzABnAldExF7ApcAcSij9ohcVlSSt3ZjsyVTmANcCNwI3AJcA8wAy8ybgQOBkYAmwK7Df8D00kqSxYcz0ZDLzEv424E9mrgAOq37WVn4BsKArlZMktWUs92QkSX3OkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1caQkSTVxpCRJNXGkJEk1WajXlegXRGxPfAVYAfgf4B/y8wLelsrSVKjvuzJRMQU4HzgHODxwAeA70bE03taMUnSo/RlyAB7AFOBf8/MhzPzP4FLgbf1tlqSpEb9erlsW+CWzBxq2HYrsF2Tx08GGBiY1HYFnrjZ1LaPHY3R1Lmfz90rtnli6FWb++17pOG4yc0e068hMx1YPmLbcmDTJo/fAmCzzZotvqZTj35V28eOxsyZ03ty3l6fu1ds88TQqzb38ffIFsBvminYryHzAOVyWaNpwLImj78K2BW4B1jVwXpJ0ng2mRIwVzV7QL+GzM3AkSO2zQIub/L4lcDPOlojSZoYmurBDOvXkPkpsCoiPgScCLyaMhng/T2tlSTpUfpydllmPgS8Btgb+BPwKeAfMvOOnlZMkvQok4aGhjZcSpKkNvRlT0aS1B8MGUlSbQwZSVJtDBlJUm36dQpzrZpd4Tki/g74OvBSYAlwbGae1s26dkoLbd4GOBnYmXK/0TnAkZm5oovV7YhWV/KOiAHK9PmbMvOw7tSys1r4Pc8APg/sBwwBZwPvz8yHu1jdjmihzc8GTgFeRLnh+zTgoyOWr+orEbETcGFmbr6O/bV/h9mTGaHFFZ7PotwYOhP4R+DEiNilW3XtlBbbfB5wI/Bk4IWUsPlYl6raMW2u5H0U8PIuVK8WLbb5G1WZp1PWCnwxcESXqtoxLbb528AlwOaU3/NBwAHdqWnnRcRs4GJgynqK1f4dZsisqakVniMiKP/jzcnMhzLzcspf0nd1u8Id0GybnwDcBXy8avM9wBn05xdvSyt5R8RLgLcCC7pXxY5r9ve8BfB64NDMXJqZfwReB5zZ7Qp3QCu/51mU78Th78UhoO966AAR8Qngg8DH11OmK99hhsyaml3heVvg7sxcuoFy/aCpNmfmnzLz7zNzGUBETKJ8+VzftZp2TtMreUfEY4HTgXdQLqP0q2bb/ALgd8DbIuKOiLgbeF+1rd+0smL7J4A5lGC5HfhRZp5dfxVrcUpm7ghcs54yXfkOM2TW1OwKz6NdCXosabkt1fjEycCz6cPLZbTW5i8B38zMX9Zeq3o12+bNgacCzwG2B3aj/GPiA3VXsAat/J4foVwSnE4J2r0i4tB6q1ePzPx9E8W68h1myKyp2RWeR7sS9FjSUluqQeHvA68EXpGZi+qtXi2aanNEHEgZlzi+S/WqU7O/55WU1XY/kJnLMvN24CTgjfVXseOa/T3vSAnRkzJzRWZeD3wGeE9XatkbXfkOM2TWdDMQI7bNqraPLPfUiJi+gXL9oNk2ExFPAa6kDCbu0sfrxTXb5rdRZiUtiYg/UwZH3x0RP6i/ih3XbJtvrV43adjWrzNRm23z01lzgPzh6me86sp3WL/+xalTUys8Z2ZGxHXACRFxOKV7/TZg3y7XtxOaanM1U+dCyuyyAzKzn5/F0+zv+dWN7yNiPrCsT6cwN9vmmyLiauAzEfEeYBD435RpwP2m2RXbf169fjwijgG2Ag6nTOMel7r1HWZPZoT1rfAcEW+LiMau5JuAZwCLgO8Ah2dm3z2npoU27wM8l3J9/i8Rsaz66buxihZ/z+NCi21+LTCJMgB+FeXy6Oe6XOVRa7bNmfmHqtwrqnIXA6cyzkKmF99hrsIsSaqNPRlJUm0MGUlSbQwZSVJtDBlJUm0MGUlSbQwZSVJtvBlTqlFE7EdZmmQn4HHAvcAvgVOrFYGlcc37ZKSaRMTngcOAO4EfU27yewrlxsDNga9l5nheG0syZKQ6RMTulCVNvge8JTMfadj3uGrfC4DXZ+Z5Pamk1AWOyUj12Kd6/UJjwABk5l+AD1Vv+3FlY6lpjslI9di4et2e8kjfkS4DZgMLhzdUD4E7lDKGsy3l4VmXAXMz87qqzBTgWsqDpR7VC4qIjwDHAV/PzHd3uD1SW7xcJtUgIvahPFv+IeCrlEcX/3J9K1dHxDeBtwP/D/gR5eFRsylL7u+dmT+pyr0YuILypMrnZOYDEbE9cDVwN7DD8NNLpV4zZKSaRMQpwHsbNt0P/IwSIOdk5t0NZd8MnEUJo4OGL7FFxFaU8HgQeGa1qjARMY/yDPdPAx+hzFjbAdgtM3+ONEY4JiPVJDP/mTI2cyHl4VePpSyh/+/AHRFxfPUYa4CDq9d/bRzDqR4K9yXKrLS9Gj7+GMrDxf6V8pyXFwCfMmA01tiTkbqgevrgbsCewH7A1tWuT2XmByNiMeWZ6yes5fDnUp77cXRmHtfwmbtQHrY1ANwA7DTc05HGCkNG6rJqgP9gyljNCuAJwF/Y8EScEzPz8IbP2ZjSm3km5ebOQ+qpsdQ+Q0bqsIh4LHAN5Qm3+6yn3EXAqyjPoP8FsDQzn9bCeY4B5lJWEdgM2DMzfzqKqksd55iM1GGZeT9lCZlXRsST1lN0CFhNefTtjcBTI+LJIwtFxN4R8YmI2KFh2w6UAf9fAS8HVgKnRsSmnWuJNHqGjFSPL1CmHp8TEVuM3FmtabYX8P0qlOYDk4AvVPfCDJfbAvgy8GFgabVtI+A0yuW192TmLcAnga2AeTW2SWqZl8ukGlRB8B1gf2A5cBHwa8pNmjsDL6OMp+yWmYurcZoFwOurchdRQmQ2MBP4UGaeUH32HOBY4CuZ+U/VtinA9cAsYPfM/O8uNVVaL0NGqlFEvAE4gLIK8xMoN2cuBM4BTsrMBxvKTgbeB7yTMk7zIOXGzBMz89yqzPBNl/cC22bmnxuO3xW4FLgdeF5mLq+9gdIGGDKSpNo4JiNJqo0hI0mqjSEjSaqNISNJqo0hI0mqjSEjSaqNISNJqo0hI0mqjSEjSaqNISNJqs3/B5aIWh2c/K9XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Sex'].tolist())\n",
    "plt.xlabel('Sex', fontsize = 20)\n",
    "plt.ylabel('Frequency', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T02:07:51.302627Z",
     "start_time": "2019-06-19T02:07:51.099469Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Pclass'].tolist())\n",
    "plt.xlabel('Pclass', fontsize = 20)\n",
    "plt.ylabel('Frequency', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T02:07:58.519090Z",
     "start_time": "2019-06-19T02:07:58.200288Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Age'].tolist())\n",
    "plt.xlabel('Age', fontsize = 20)\n",
    "plt.ylabel('Frequency', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:37.053278Z",
     "start_time": "2019-06-17T07:22:36.846025Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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satvPSbIkyanA1q7XZiRJ/XW9a2sC+Oz97LMEeNgMj/dwmskelwC3Ay+tqmuSXAJcRxMs51ZVJbkMOAW4keZW47O7dECSNDe6BskPaG7L3ZcTaH7R36+q+ibw2Gna1wBrprRNAue3L0nSkHU9tfVx4MQk0/4yT/I6mqD5QtfCJEnjoeuI5K3A84F3JXkhzcqIJPkz4Ck0p55+2u4nSVrAOgVJVf04ydNoJm88fWDTn7R/3gC8sqq+17M+SdKI6zxFSnu31vOSPJ5m2pJDgTtp7qy6uqqmu5NLkrTAzDpIkoTmtNZdVfX5dtThyEOSFqlZX2xvRxofB3537suRJI2brndtXQ88ZC4LkSSNp65B8kfA6UleMJfFSJLGT9eL7Y8FPgx8NMl3gKtp5sCanLpjVb2ze3mSpFHXNUjeTjO/VoAT29fUu7TSthkkkrSAdQ2StzD9RI2SpEWm6wOJb5rjOiRJY2pOltqVJC1eMwqSJN9McuFetr1itsvqSpIWjpmOSI6nWaFwOuuBl89JNZKkseOpLUlSLwaJJKkXg0SS1ItBIknqZTZBsjrJT6a+aB5MnHZbkh/P5MBJ9kuyLsnWJDcneX3bfkGSW5JsSXL6wP5rk2xPcm2SlbPqsSRpTs3mgcSl7Wu222biAOAK4N8ChwDXJfkmcB7N2u9HA1cmOYZmEa2TgOXAM4CLae4qkyQNwYyCpKrm9RRYVe0GPta+/VGSW4GnAZdW1Z3A5iTbgJXAmcD6qroH2JBkIslhVXXbfNYoSZreyF0jSfIEYD+apXtvHti0HTicZnQy2L6jbZ96nNVJNibZuGvXrnmsWJIWt5EKkiSHAh+iWX1xKfedln4SuHcf7fdRVeuqalVVrZqYmJi/oiVpkRuZIEnyCOBTwEVV9Q2a9U2OHNjlKODWadqPoBmtSJKGYCSCJMlBwCeBt1XVp9vmy4GzkixLsgI4GNjUtp+TZEmSU4GtVXXHUAqXJHVej2SunQ+cALw7ybvbtmcDlwDXAXcD51ZVJbkMOAW4EdgNnD2EeiVJrZEIkqp6G/C2aTataV+D+07SBM/5D0BpkqT7MRKntiRJ48sgkST1YpBIknoxSCRJvRgkkqReDBJJUi8GiSSpF4NEktSLQSJJ6sUgkST1YpBIknoxSCRJvRgkkqReDBJJUi8GiSSpF4NEktSLQSJJ6sUgkST1MlJBkmT/JI8ddh2SpJkbiSBJclCSTwC3AxcOtF+Q5JYkW5KcPtC+Nsn2JNcmWTmMmiVJjQcNu4DWJPA3wKeA3wRIcixwHnAccDRwZZJjgJOBk4DlwDOAi4HjH/iSJUkwIkFSVXcBn0vyyoHmM4BLq+pOYHOSbcBK4ExgfVXdA2xIMpHksKq67YGuez4tf8PlQ/vubWufN7TvljR+RuLU1l4cDdw88H47cPg07TvadknSEIxykCylOeW1xyRw7z7a7yPJ6iQbk2zctWvXvBYqSYvZKAfJTuDIgfdHAbdO034EzWjlPqpqXVWtqqpVExMT81qoJC1moxwklwNnJVmWZAVwMLCpbT8nyZIkpwJbq+qOYRYqSYvZSFxsT3Ig8C3gQGC/JE8HXg1cAlwH3A2cW1WV5DLgFOBGYDdw9lCKliQBIxIk7Z1Zj5lm0xeANVP2nQTOb1+SpCEb5VNbkqQxYJBIknoxSCRJvRgkkqReDBJJUi8GiSSpF4NEktSLQSJJ6sUgkST1YpBIknoxSCRJvRgkkqReDBJJUi8GiSSpl5GYRl6jZfkbLh/K925b+7yhfK+kfhyRSJJ6MUgkSb0YJJKkXgwSSVIvBokkqZexvGsryUuAvwDuBdZU1fuHXJLGnHeqSd2NXZAkORB4B/CbNEGyKcknq2rXcCuTZm9YAQaGmObO2AUJcBpwVVXtAEjyeeBfAx8ZalXqbZi/VKX5tNBHvOMYJEcDNw+83w4cPnWnJKuB1e3bu5Js6fh9hwI/6vjZcbOY+gqLq7+/1Nf8xZAqeWAs6p/tHnPwMz5mJjuNY5AsBSYH3k/SnOK6j6paB6zr+2VJNlbVqr7HGQeLqa+wuPq7mPoKi6u/o9DXcbxraydw5MD7o4Bbh1SLJC164xgknwVOS/LIJIcBT23bJElDMHantqrqtiT/Afhq2/SHVfXTefzK3qfHxshi6issrv4upr7C4urv0Puaqhp2DZKkMTaOp7YkSSPEIFmkkuyf5LHDrkPS+DNI9iHJS5LclOT6JL837HrmQpKDknwCuB24cKD9giS3JNmS5PSB9rVJtie5NsnKYdTcVZL9kqxLsjXJzUle37YvxL7+SpINbV+3JDmtbV9wfd0jydIkm5O8r32/kPu6rf09dH2SL7dto9PfqvI1zQs4kOa24iOBw4DbgIlh1zUH/XoozUwA5wLva9uOBba2fV4B/AB4MPBM4GqamzJOBTYNu/5Z9vUQ4EVAaB7auh04ZYH2NcDh7d+fA2xcqD/XgT6/CfgfwPsWQV+3TXk/Uv11RLJ3/28qlqq6DdgzFctYq6q7qupzwD0DzWcAl1bVnVW1GdgGrATOBNZX1T1VtQGYaG+5HgtVtbuqPlaNH9H8j8HTWJh9rara2b49Bvg2C/TnCpDk8cCJwKVt04Lt616MVH8Nkr2b0VQsC8Te+jq1fQdj+s8gyROA/WhGJguyr0kuTLIbeD3wFhbozzVJgPcAFww0L8i+DvhZkhuSfK09bTlS/TVI9m5GU7EsEHvr64L4Z5DkUOBDwO+ygPtaVX9ZVYcAFwFXsHD7+hrgi1V1/UDbQu0rAFX1+Ko6Fvhj4B8Ysf4aJHu3mKZi2Vtfp7YfQfN/PmMjySOATwEXVdU3WMB93aOqPk5zLWyh9vXlwFlJNtGMvM6guYa5EPt6H1X1ZZrTWCP1szVI9m4xTcVyOc1/mMuSrAAOBja17eckWZLkVGBrVd0xzEJnI8lBwCeBt1XVp9vmhdrXX9tzLjzJU4C7WaB9raqnVtUTq+p44E+By2j+Z2HB9RUgyQFJDm//fgLNqarPMUL9HbspUh4o9cBPxfKAaBcG+xbN3R77JXk68GrgEuA6ml9A51ZVJbmM5i6nG4HdwNlDKbq784ETgHcneXfb9mwWZl8fDnwmyRKau9NeWlXXJFmIff0lC7yvy4Cr2p/tj4GXVdVXRqm/TpEiSerFU1uSpF4MEklSLwaJJKkXg0SS1ItBIknqxSCRJPVikEiSejFIpA6S1D5em4Zdn/RA8sl2qbtfAOumaR/buZykLnyyXeogSQE/raqHDrsWadg8tSWNsDT871QjzX9BpXnUzsL6yiRXJtmd5BftOtvvTLJsyr5vaq+x/FGS5yf5Ps1Kli8Y2OfhSd6e5KYkP2/X5n5vu+aKNBQGiTS/ngN8AHg08GngH2kWH3o9zWJb03lsu993gY8AdwG008R/HfhDYBfwYeCHwO8DV7czO0sPOC+2S/PrR8AZVfWJPQ1tIGwFzkzyqKq6ZcpnzgHOqqrLprRfTBMyf1BV72qPFeBvacLkQuCN89MNae8ckUjdHbCX239fuWeHqvr6YIi0bbcBV7Vvj5vmuN+ZGiLt4kXPBa7aEyLtsQp4a/v2hf27JM2eIxKpu73d/vu9wTdJDgBOAp4MHNu+ntRuXsYvu3qatpP37J/kb6fZXu1xpQecQSJ19y9V9dp97ZDkecD7gUcCPwA2t6+H0oRJpvnYzmna9lxMP7F9TWf/GdQszTlPbUnzJMkEzQXxpcBvVdWRVXVqVa2mCZO9mZym7c72z7VVlb295rgL0owYJNL8eTJwEPBPVfU/p2z79Vkea8+0K0/pXZU0xwwSaf78ov3ziUmW7GlM8nqmv8i+L1cDNwCnJDl/6sYkpyR5dOdKpR68RiLNn68AtwAnAN9J8nVgBbAcuAI4baYHqqrJ9m6wK4C/TrIa+BZwL801kxXAbwI3zWH90ow4IpHmSVXdBTwb+CTwq8CLgNuB3wJu63C8q4HfAD4KTAC/Azy/PebvAd+Yk8KlWXLSRklSL45IJEm9GCSSpF4MEklSLwaJJKkXg0SS1ItBIknqxSCRJPVikEiSejFIJEm9GCSSpF4MEklSL/8XqXXaHekKlEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Fare'].tolist())\n",
    "plt.xlabel('Fare', fontsize = 20)\n",
    "plt.ylabel('Frequenc y', fontsize= 20)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:37.928389Z",
     "start_time": "2019-06-17T07:22:37.055292Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Fare'].tolist(), bins = 50)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Fare', fontsize = 20)\n",
    "plt.ylabel('Frequency', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:26:13.751305Z",
     "start_time": "2019-06-17T07:26:12.413595Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LtyS7JjlomMFIkiRJkmY3b/GW5AlJPgvcCbyvp/2UJLcmuSHJMT3tZybZlGRDksOHE7YkSdLkSLJLkrVJbkxyS5JT23b3tyT9VD/XvG0DzgEuB34VIMnTgHcAhwAHAFcmORB4AfB8YBVwBHAecNjAo5YkSZosy4HPA28F9gSuS/It3N+S1GPeM29VtaWqvghs7Wk+Frioqu6vquuBjcDhwHHAuqraWlXrgb2TrBxC3JK0XR7BltQ1VXVPVV1cjbuB24Bfx/0tST12dLbJA4Bre55vAvZp2y/tad/ctt/R++YkJwEnAaxYsYKpqak5P2zLli3zLjNOTjt067zLrNi1v+W6pmvbqh/2qZM8gi2ps5I8E9gF2ItF3N+aNox9lB39P2ec/r8ap1hgvOIxltkNI5YdLd6W0QynnLYNeGiO9oepqrXAWoDVq1fXmjVr5vywqakp5ltmnJzQxxT8px26lbM3TN6dGtYdvbxT26ofXfv968ck9qlXVd0DXNw+vTvJw45gA9cn2ciMI9jA+iR7J1lZVXfMtm5JGqYkewHnA28C3swi7m9NO+fCSwe+j7Lxdf199kzj9P/VOMUC4xWPscxuGLHs6F/m7cB+Pc/3pzm9P7N9X5qjRJI0Eh7Bnt84HaXcHmMcnC7E2YUYhyHJz9HMMfDBqrqqHd7t/pakn9rR4u0K4PwkZ9EMNdoDuKZt/60kFwIvAm6sqnsHEagkLZRHsPszTkcpt8cYB6cLcXYhxkFL8gTgMuD0qvpc2+z+lqSHmXePIsnuwD8AuwO7JFkDvAW4ALgOeBA4saoqySXAC4GbgXuA44cUtyTNySPYkjrmZODZwCeSfKJteynub0nqMW/x1l4f8vRZXvoycMaMZbfRJJ+TBxKdJO0Aj2BL6pqqOh04fZaXzsD9LUmtyZsxQ5I8gi1JkiaQxZukieMRbEmSNInmvUm3JEmSJGn0LN4kSZIkqQMcNqmB2rD5vr5uUr4QG8982UDXJ0mSJHWRZ94kSZIkqQMs3iRJkiSpAyzeJEmSJKkDvOZNkiRJQ7VqB6+HP+3Qrdu9lt5r4rUUeeZNkiRJkjqgM2fedvSIzXw8aiNJkiSpCzpTvEmSGoM+mHXaoVtZM9A1SpKkYVjyxduwzuhJkiRJ0iAt+eJN428YBbbDZSVJktQ1Fm+SJEkScx8wnmvmy7l4wFiD5GyTkiRJktQBFm+SJEmS1AEWb5IkSZLUARZvkiRJktQBFm+SJEmS1AHONqklaSG3H+h3dilnk5IkSdIweeZNkiRJkjrA4k2SJEmSOsBhk5KkBQ0l7pdDiSVJGqyBn3lL8uok30tyU5I3D3r9kjQM5i5JXWPekpaegZ55S7I7cDbwq8BDwDVJLququwb5OdI4GsaZC/DsxWIwd0nqGvOWtDQNetjkUcBXqmozQJIvAS8GPj3gz5GkQTJ3dYTDO6WfMm9JS9Cgi7cDgFt6nm8C9hnwZ0hLyjB2VtcdvXzg6+w4c9cQDPJ3t99bduyIQcXZG+NSLwgf7Xc62/Ze6t/pLMxbS9yGzfcNLS8u1Fw5ehh/u3PlmB39/6IrOSZVNbiVJe8HHl9Vv9c+PxP4p6r65IzlTgJOap/+AnDDPKveC7h7YIGOh0nsE0xmv5Zynw6sqr2HHcyo9ZO7diBvTevC748xDkYXYoRuxPloY5z43LWE8paxbN84xWMss1tILH3lrUGfebsdWNPzfH/gf81cqKrWAmv7XWmSb1bV6kcd3RiZxD7BZPbLPi0J8+auheataV34ro1xMLoQI3Qjzi7EOAaWRN4ylu0bp3iMZXbDiGXQs01+ATgqyZOTrASe27ZJ0jgzd0nqGvOWtAQN9MxbVd2R5HeAr7dNp1XVA4P8DEkaNHOXpK4xb0lL08Bv0l1V64B1A17tgk/5d8Ak9gkms1/2aQkYUu6CbnzXxjgYXYgRuhFnF2IcuSWSt4xl+8YpHmOZ3cBjGeiEJZIkSZKk4Rj0NW+SJEmSpCGweJOkJSLJrkkOGnUcktQv85b0cGNfvCV5dZLvJbkpyZtHHc+OSLJLkrVJbkxyS5JT2/ZTktya5IYkx4w6zh2RZFmS65Oc2z7vdJ+SPDHJp5NsTvLdtn9d79N7knyn/Tt6R9vW6T51wTjlriRPSPJZ4E7gfT3ts/4eJDkzyaYkG5IcvkgxLihPjijGxyRZ38Z4Q5Kjxi3Gns/uKzePOMaN7d/HTUm+Oq5xLiVdyFsjimXW/DSiWGbNQ6M0M9+MOJZH5JURxvKIfcqBrLiqxvYB7A7cBuwHrATuAPYedVw70I89gVcCoblZ353AC4Eb2z4eDPwT8LhRx7oDffsw8DfAucDTut4n4M+B32231S5d7xOwCtgILG9/D+8DDulyn7rwGLfcBTweeDFwInBu2zbr7zbwIuB/0kxodSRwzSLF2HeeHGGMAfZpfz4a+Oa4fY89sc6bm8cgxo0zno9lnEvl0YW8NcJYZstPB4wolkfkoTH43flpvhmDWDaOOoaeWGbuU2YQ6x33M29HAV+pqs1VdQfwJZo/5E6pqnuq6uJq3E2THH8duKiq7q+q62l2sDt1NDHJM4BfBi5qm46lw33Kz+6Tc0a7rR6k430CftL+u41mx+d+4F/T7T51wVjlrqraUlVfBLb2NG/vd/s4YF1Vba2q9cDe7d/GsGNcSJ4cVYxVVbe3Tw8Evs2YfY+woNw8shi3oytxTqou5K1RxTJbfnrSiGKZLQ+NzCz5Rsy+T1ltRfdojXvxdgBwS8/zTcA+I4plIJI8k6b63osO9y1JgE8Cp/Q0d317HQJ8D7i4HYpwFh3vU1Vtpjki9g3gSuB4YH863KeO6MLvzfZinNm+mUWOvY88ObIYk7wvyT3AqcBHZollpDEuMDePelv/uB1K9I126Ne4xrlUdCFvjVxPfrp2hDHMzEOjimO2fDNqM/PKqDxin7L9vh61cS/eltGcMZi2DXhoRLE8akn2As4H3kT3+/Y2YKqqbupp63qfnkwzVOddwHOA5wH/hg73KckTaAq2U4A/BN5L97dTF3ThO95ejCONvc88ObIYq+pjVbUn8EHg82MY40Jy80i3dVU9o6qeBvw2cOG4xrmE+D3Pozc/Deosyo6YmYcGVRTsgNnyzUjNzCtJRnKGlNn3KV8xiBWPe/F2O83Y62n705yq7pwkPwdcDnywqq6i+317A/CaJNfQHPU5lmZ8fJf79H3g6qraVFUPAOtpbn7a5T69HvjHqpqqqv/RtnV9O3VBF/6+txfjzPZ9aY7AD90C8uTIYpxWVX9Nc03OuMW4kNw88u8RoKq+SjNEcty+y6WmC3lrZGbJTyPXk4f2HFEIj8g3SX57RLE8TE9eWTWiEGbbp/yFQax43Iu3LwBHJXlyz9jRL4w4pgVrz35cBpxeVZ9rm6+g+YXfLcnBwB7ANaOKcaGq6rlVdWhVHQZ8CLiEJql1tk80QwsPTrJvkp2BlwBb6HafHgQOS/K4JLsDB9EMn+xyn7qgC7lreznoCuCNSXZKciRwY1XdO+xgFpgnRxXjU6evtUryazR/X2MV4wJz80hiBEiyPMk+7c/Pphme98Vxi3OJ6ULeGont5KdRxfKIPNReh7foZss3VfXxUcQC280r3xlROLPtU35zECt+7CBWMixVdUeS3wG+3jad1lavXXMy8GzgE0k+0ba9FLgAuI5mB+DEUZ6CH4SqujpJZ/tUVQ8keRfN0ZGdaS6QP7v9o+tkn2h+x14E3Az8GPizqvr7Lm+nLhi33NUW7v9AM5vcLknWAG9hlhyU5BKaWR5vBu6hGXa7GPrOkyOM8UnA3ybZiWa2ud/YXt4bYYyPMKYx7gZ8pf0u7wNev73cNE7f5STrSt6qqi+PIJxZ81NV3TyCWB6Rh0YQw7iaLa+M5Hd4O/uUA/ndjftskiRJkjT+xn3YpCRJkiQJizdJkiRJ6gSLN0mSJEnqAIs3PUySjUkqybYktyT5cJLt/p4kWdfezFqSxlKSX0xyeZIfJPlRkq+MOiZJgtnzUztD4U09MyduTPLyOdbxr5P8v0keSHJfkv+yeD3QYrN402xeQXOzzn9Pc6Pct402HEnaMe1ssV+guaH2/sBTgHNHGpQksf38VFX/VFVPr6rb+1jHs4A/Az5Ac7+35/CzGUM1gcb6VgEanaraClyV5FzgSOCPRxySJO2IQ4C9q+qc9vkDwPkjjEeSpg0iP72Y5mbQX2qff7d9aEJ55k3z2RX45/bmqB9sT+P/OMmnZy6YZEWSv05yT5K7knyk57VXJrkhyYNJvt62PTHJZ9pT/Pcm+feL2C9JS8NmYDp/pfeFdrjSVJJ/TvL1JIe07Zcn+ZP2552SXJ/kTSOIXdJkmzU/JVnVXsLy+J5l92/z1QNJ/i7JU9v2jcCvtzewf5gkJyT5RpJTk3w/yT8lef9Qe6Shs3jTrJIsa2+IeSLN6fjfA14PvBrYm9nPxO0F/D/AvwKOAt6f5LAkuwF/CZwC7AFMJ47fbp8fAPwSzY1ZJWlgqupO4O3A79OMJvgVgDYvrafJWfsBlwKfaa/xfTdwfJKnAG8G7gbWLX70kibZ9vLTdpxCs990AHA78Odt+2eBC4EvJPl0kv1mvO8QYCXNvtnxwO8lecngeqHFZvGm2VwGbAH+CHh7eyr+3cA7q+pbVbWlqv5u5puq6rqqWgc8CVhBs8NzMBBgK81Y7gd73vsQTfH2pKraVFX/e8j9krQEVdV5NAeI7gK+luRdNNf23lVVf1hV9wMfp9m5eUpV3QR8EjgT+B2aPFijiV7SJNtOfprNH1fVVVV1L/A+4HlJnlRV26rqLcAx7XquS/Lcnvf9CPiPVXVfVU0BfwVsd/ITjT+LN83mFVW1rKqeVVUXJ9kbeCKwYa43JXlekpuAtcDL2uZlVfUAzY7S24HvJHlV+9pHga/RJJrzk+w5lN5IWvKq6oaqOgZ4J/CHwC8Az26HJhXNAaZlwL7tWz4OHAdcVVWOCpA0NLPkp5+fZbHv9fx8Z/vvHj3r+FvgWTQH4P9Hz7K3VdW2Ge/dA3WWxZv68QPgX4CnzrPcx4E/rKqXVNU7gQenX6iqL1bVLwHvAf4iyVOq6kdV9S5gFfDk9v2SNDRV9SmakQUAX62qzHh8tX3tZJphlS9O8vSRBCtpSZmRn2bqPcD9DJrRS5tmvP8nwFnAU5PsNMv7pt/7PdRZFm+aVzvz5PnAJ5L8QpInJHnpLIs+jiZhLE/yDpppb0myZ5J/1154+x2gaC7QfVl7we2PaBLQskXpkKQlI8nqJKckWZlklySvBbYB/zdwaHtB//IkP9++RpJnAKcCbwX+G862K2kI5shP/zzL4qcl+Vftvd/OAi6sqn9J8vokxyXZPcmTaG7x9NWqeqh934FJ3tO+/iqaOQmccbfDLN7Ur3cD1wB/B9zG7OOl/yPwSuAOmgkAeodZfpjmGri/Ad7dXlNyEHAVzTjvfWnuUSJJg3QvzURLN9AMF3or8PKquodmOPe7aHLQl4G92hnf/jvwyaq6DfgvwLOmCztJGqBZ8xPww1mWvRi4HLieZj9r+tq4+4A/aN9/I82cA6/ved81wNPb93wEeHW7D6aOitdgS5IkSZMlyQk0k82tHnUsGhzPvEmSJElSB1i8SZIkSVIHOGxSkiRJkjrAM2+SJlKSJyb5dJLNSb6bZFk7q9etSW5IckzPsmcm2ZRkQ5LDRxm3JEnS9jx21AFI0pCcA1wLvBbYGTgAeAdwSPvzlUkOBF4APJ/mfoNHAOcBh40gXkmSpDn1Vbwl2QhsbZ/eXlUvSHIKcBrwY5qp3z/XLnsmzRSlPwBOqKqr51r3XnvtVatWrZo3hgceeIDly5f3E25n2KduWMp9uvrqq++uqr0XIaSBSrISeC5NDirgwSTHAhdV1f3A9W1eOxw4DljX3s9wfZK9k6ysqju2t/5+8xZ05/fHOAerK3FCd2JdSJxdzV3DNIl5ayHs0/ibtP7AcPJW32fequrp0z8neRoDOoK9atUqvvnNb877+VNTU6xZs6bfcDvBPnXDUu5TkluGH81QHAJ8D7g4ycHAZTQ3kb+2Z5lNwD40OezSnvbNbfvDirckJwEnAaxYsYKzzjqrr0C2bNnC4x//+B3rxSIyzsEbhXh+AAAgAElEQVTqSpzQnVgXEucRRxzR1dw1NP3ub8HS/n+vSyatT5PWH1hYn/rd59rRYZMDO4ItSUPwZOBg4FdoRgFcCawE/rFnmW3AQ8Cy9ueZ7Q9TVWuBtQCrV6+ufpNxV/4zMs7B6kqc0J1YuxKnJA1Tv8Xbj5N8F7gL+H2aI9U7fARbkobs+8DVVbUJIMl6moJsv55l9gduA26f0b4vTU6TJEkaK30Vb1X1DIAkLwAuAf6K2Y9U93UEe+bwo6mpqXlj2LJlS1/LdYl96gb71EnfAP4kyb7APcBLgIuB30xyFs3Q7j2Aa4ArgN9KciHwIuDGqrp3JFFLWtKSPBH4E5rLUB4EngG8nQHMMSBpMixo2GRVfbUdIjnzSPWCjmDvyPCjSRwuYZ+6wT51T1U9kORdwHqamSbXVdXZSXYGrqPZKTqxqirJJcALgZtpCr3jRxW3pCXPWXIlzWne4i3JcuAJVXV7kmfTDIP8IvDfPYItDd+qD1wx8HWuO3qyZnOaTXt0+nMz2s4AzpjRtg04uX0M3IbN93HCgLfhxjNfNtD1SRq9Yc+SuxDmLWl89XPmbTfgK0l2Au4DXl9Vf5/kAjyCLUmSNAhDnyW33+HyK3aF0w7dOv+CCzDqofqTeLnApPVp0voDw+nTvMVbVd0FHDRL+6IfwZYkSZpQYzNL7jkXXsrZG3Z0QvLZbXxdf589LJN4ucCk9WnS+gPD6dNg/zIlSZK0I5wlV9K8HjPqACRJksQ3gIOT7NtOrvQSYAvwmiS7tUMpe+cYeGOSnZIciXMMSEuGZ94kSZJGzFlyJfXD4k2SJGkMjMssuZLGl8MmJUmSJKkDLN4kSZIkqQMs3iRJkiSpAyzeJEmSJKkDLN4kSZIkqQMs3iRJkiSpAyzeJEmSJKkDLN4kSZIkqQMs3iRJkiSpAyzeJEmSJKkDLN4kSZIkqQP6Kt6SLEtyfZJz2+enJLk1yQ1JjulZ7swkm5JsSHL4sIKWJEmSpKXmsX0u90FgI0CSpwHvAA4BDgCuTHIg8ALg+cAq4AjgPOCwwYYrSZIkSUvTvGfekjwD+GXgorbpWOCiqrq/qq6nKeoOB44D1lXV1qpaD+ydZOVwwpYkSZKkpWXOM29JAnwSeDvNWTVozrZd27PYJmCftv3SnvbNbfsds6z3JOAkgBUrVjA1NTVvoFu2bOlruS6xT90w6j6ddujWga9z1H2SJEnSws03bPJtwFRV3ZRkunhbBmzrWWYb8NAc7Y9QVWuBtQCrV6+uNWvWzBvo1NQU/SzXJfapG0bdpxM+cMXA17nu6OUTt50kSZIm3XzF2xuA3ZO8CtgDWE5zJm6/nmX2B24Dbp/Rvi/NWTlJkiRJ0qM05zVvVfXcqjq0qg4DPgRcAlwOvCbJbkkOpinqrgGuAN6YZKckRwI3VtW9Q45fkiRJkpaEfmeb/KmqujrJBcB1wIPAiVVVSS4BXgjcDNwDHD/QSCVJkiRpCeu7eKuqdcC69uczgDNmvL4NOLl9SJIkSZIGqK+bdEuSJEmSRsviTZIkSZI6wOJNkiRJkjrA4k2SJEmSOsDiTZIkSZI6wOJN0sRKsizJ9UnObZ+fkuTWJDckOaZnuTOTbEqyIcnho4tYkiRp+xZ8nzdJ6pAPAhsBkjwNeAdwCHAAcGWSA4EXAM8HVgFHAOcBh40gVkmSpDl55k3SREryDOCXgYvapmOBi6rq/qq6nqaoOxw4DlhXVVuraj2wd5KVo4hZkiRpLp55kzRxkgT4JPB2mrNq0Jxtu7ZnsU3APm37pT3tm9v2O2as8yTgJIAVK1YwNTXVVywrdoXTDt264D7Mpd/PXogtW7YMZb2DZpyD15VYuxKnJA2TxZukSfQ2YKqqbkoyXbwtA7b1LLMNeGiO9oepqrXAWoDVq1fXmjVr+grknAsv5ewNg021G1/X32cvxNTUFP32aZSMc/C6EmtX4ny0kiwDrgG+VlUnJjkFOA34MfDuqvpcu9yZwOuBHwAnVNXVo4pZ0uKxeJM0id4A7J7kVcAewHKaM3H79SyzP3AbcPuM9n1pzspJ0ih4ra6k7fKaN0kTp6qeW1WHVtVhwIeAS4DLgdck2S3JwTRF3TXAFcAbk+yU5Ejgxqq6d2TBS1qyvFZX0nw88yZpSaiqq5NcAFwHPAicWFWV5BLghcDNwD3A8SMMU9IS5bW6wzWJ10xOWp8mrT8wnD5ZvEmaaFW1DljX/nwGcMaM17cBJ7cPSRoVr9Udokm8ZnLS+jRp/YHh9GneYZNJHpNkfZIb2xvbHtW2e7NbSZKkwXgDzdDua4CP0AyZvAOv1ZXUo5/DKgX8h6q6PcnRwH9OchNeQCtJkjQQVfXc6Z+TnECzP3U5cH6Ss2j2rXqv1f2tJBcCL8JrdaUlY97iraqK5ggPwIHAt+m5gBa4PslGZlxAC6xPsneSlVV1xyyrliRJ0nZ4ra6kmfoa0JzkfcD7gbuAo4D3sMgX0HoRYzfYp8Eb9EXjMPo+SZK2z2t1JW1PX8VbVX0M+FiS44DPA19mkS+g9SLGbrBPg3fCB64Y+DrXHb184raTJEnSpFvQfd6q6q+Bx/PIC2W9gFaSJEmShqif2SafOn3jxyS/RjPm+gq82a0kSZIkLZp+hk0+CfjbJDsBdwK/4QW0kiRJkrS4+plt8lvAQbO0ewGtJEmSJC2SBV3zJkmSJEkaDYs3SZIkSeqAvm4VIEmS1K9VQ7rFiSQtdZ55kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA547KgD6NeGzfdxwgeuGPh6N575soGvU5IkSZIGbd4zb0l2SbI2yY1Jbklyatt+SpJbk9yQ5Jie5c9MsinJhiSHDzN4SZIkSVoq+jnzthz4PPBWYE/guiTfAt4BHAIcAFyZ5EDgBcDzgVXAEcB5wGGDD1uSJEmSlpZ5z7xV1T1VdXE17gZuA34duKiq7q+q64GNwOHAccC6qtpaVeuBvZOsHGL8kiRJkrQkLOiatyTPBHYB9gKu7XlpE7APzVm4S3vaN7ftd8xYz0nASQArVqxgampq3s9esSucdujWhYTbl34+e1i2bNky0s8fBvs0eMP4vR91nyRJkrRwfRdvSfYCzgfeBLwZ2Nbz8jbgIWDZdtofpqrWAmsBVq9eXWvWrJn388+58FLO3jD4+VU2vm7+zx6Wqakp+ul7l9inwRvGRD3rjl4+cdtJkiRp0vV1q4AkPwdcDnywqq4Cbgf261lkf5rhlDPb96U5KydJkiRJehT6mW3yCcBlwOlV9bm2+QrgNUl2S3IwsAdwTdv+xiQ7JTkSuLGq7h1S7JIkSZK0ZPQzDvFk4NnAJ5J8om17KXABcB3wIHBiVVWSS4AXAjcD9wDHDz5kjbNh3I/Pe/FpoZLsAnwSWAPsDHyiqv6vJKcApwE/Bt49fUAqyZnA64EfACdU1dUjCVySJGkO8xZvVXU6cPosL53RPnqX3UZT7J08kOgkacd4ixNJkjRx+rrmTZK6xFucSOqaJLskWZvkxiS3JDm1bT8lya1JbkhyTM/yZybZlGRDksNHF7mkxTT46RslaYyM8hYnMJzbnAzjNg9duX2EcQ7eMGL1Fic7ZKJHDKwawszJXlahpcjiTdLEGvUtTmA4tzkZxi1ORn1LjH4Z5+ANI1ZvcbJwVXUPcHH79O4kDxsxAFyfZCMzRgwA65PsnWRlVd0x27olTQ6LN0kTaeYtTtrhRt7iRNLYm8QRA8OwkDOxk3jmdtL6NGn9geH0yeJN0sSZ4xYn5yc5i2aoUe8tTn4ryYXAi/AWJ5JGaFJHDAzDQkYhdOlseL8mrU+T1h8YTp/G/y9TkhbOW5xI6hxHDEiaj7NNSpo4VXV6VS2vqqf3PG6uqjOq6ilV9Yyq+vt22W1VdXJVHVhVz6mq/2/U8UtaeuYYMfCaJLslOZiHjxh4Y5KdkhyJIwakJcMzb5IkSaPniAFJ87J4kyRJGrGqOh04fZaXzmgfvctuoyn2Tl6E0CSNEYdNSpIkSVIHWLxJkiRJUgdYvEmSJElSB1i8SZIkSVIH9F28Jdk1yUHDDEaSJEmSNLt5i7ckT0jyWeBO4H097ackuTXJDe1NJKfbz0yyKcmGJIcPJ2xJkiRJWlr6uVXANuAc4HLgVwGSPA14B3AIcABwZZIDgRcAzwdWAUcA5wGHDTxqSZIkSVpi5j3zVlVbquqLwNae5mOBi6rq/qq6HtgIHA4cB6yrqq1VtR7YO8nKIcQtSZIkSUvKjt6k+wDg2p7nm4B92vZLe9o3t+139L45yUnASQArVqxgampq3g9csSucdujWeZdbqH4+e1i2bNky0s8fhmFsp1F/R6PeTsP4vR91nyRJkrRwO1q8LaMZTjltG/DQHO0PU1VrgbUAq1evrjVr1sz7gedceClnb9jRcLdv4+vm/+xhmZqaop++d8kwttMotxGMfjud8IErBr7OdUcvn7jfPUmSpEm3o7cKuB3Yr+f5/sBts7TvS3NWTpIkSZL0KOxo8XYF8JokuyU5GNgDuKZtf2OSnZIcCdxYVfcOKFZJkiRJWrLmHd+WZHfgH4DdgV2SrAHeAlwAXAc8CJxYVZXkEuCFwM3APcDxQ4pbkiRJkpaUeYu3qrofePosL30ZOGPGstuAk9uHJEmSJGlAdnTYpCRJkiRpEVm8SZIkSVIHWLxJkiRJUgdYvEmSJElSB1i8SZIkSVIHWLxJkiRJUgdYvEmSJElSB1i8SZIkSVIHWLxJkiRJUgdYvEmSJElSB1i8SZIkSVIHWLxJkiRJUgc8dtQBSJIkSQu16gNX9L3saYdu5YQ+lt945sseTUjS0HnmTZIkSZI6wDNvkiRJUsds2HxfX2cTF8Izj+Nv4MVbklcDHwUeAs6oqj8d9GdI0qCZuyR1jXlr8BYyFLNfFkQapIEWb0l2B84GfpUmkVyT5LKqumuQnyNJg2TuktQ15i1psIZRuK87evnA1znoM29HAV+pqs0ASb4EvBj49IA/R5IGydwlqWvMWxo4zzyOv0EXbwcAt/Q83wTsM3OhJCcBJ7VPtyS5oY917wXc/agjnBnLRwe9xgUZSp9GbOB9GvE2ggncTkd8tO8+HTjsWMbEvLlrB/MWdOdvoiu/58Y5eJ2IdQF5C5ZG7upU3hq1k0fYpyHux3RiOy2g/53oz0IMI28NunhbBmzreb6N5lT+w1TVWmDtQlac5JtVtfrRhTde7FM32KclYd7ctSN5C7rzXRvnYHUlTuhOrF2JcxEt+by1EPZp/E1af2A4fRr0rQJuB/breb4/cNuAP0OSBs3cJalrzFvSEjTo4u0LwFFJnpxkJfDctk2Sxpm5S1LXmLekJWigwyar6o4kvwN8vW06raoeGNDqF3zavwPsUzfYpwln7gKMc9C6Eid0J9auxLkozFsLZp/G36T1B4bQp1TVoNcpSZIkSRqwQQ+blCRJkiQNwdgWb0l2TXLQqOMYpEnskyRJw+D/mZL0SGNXvCV5QpLPAncC75vl9Wcm+XaSW5Kck2Ts+jBTH31al2Rzkpvax88vfpQLk2SXJGuT3Nhui1NnvN6p7dRHf7q4jR6TZH3bpxuSHDXj9U5to3GX5NVJvtf+frx5xmtj813PE+cpSf53G+f5SQZ9O5kFmSvWnmXOS3LTYsc2I4Y540zy4SS3JdmY5LmjiLGNY65t/5IkG9rX/zTJTiOMc+L2A7qin7+5Lpnv//YuS7IsyfVJzh11LIOQ5IlJPt3ua303ybJRx/RoJXlPku+0f1PvGNiKq2qsHsDjgRcDJwLnzvL63wHHADsBXwH+3ahjHkCf1gFrRh3nAvu0J/BKIDQ3VbwTOKCr26mP/nRxGwXYp/35aOCbM17v1DYa5wewO80U3fsBK4E7gL3H7bvuI84309w76rE0s9a9bly/03aZI4ArgJvGNc72O70c2LX9m9xlTOP8HvDM9nf0fwLHjPA7nbj9gC48+vmb69pjvv/bu/wAPgz8zWx/I118AH8O/O50nqSdl6OrD2AVsBFY3v4e3gcsH8S6x+5oVVVtqaovAltnvpZkb+ApVfW5qnoIuJBmp3SszdWnrqqqe6rq4mrcTZPwnwTd3E5z9aer2r7c3j49EPj29Gtd3EZj7ijgK1W1uaruAL5Es/M5bt/1duMEqKo/rap/qaqtwD8Ce4woTpgn1iS7AKcDvzei+KbNGSdwKvDuqvpx+zf54EiinD/O/9Pz887A9xczuF6TuB/QEfP9jnTOJP7fDpDkGcAvAxeNOpZByM9udXHGdJ6stgLqsJ+0/26jOSB6P/Avg1jx2BVv89gfuLXn+SZgnxHFMkg/Af4syXVJTht1MAuV5Jk0R0mubZs6vZ1m6Q90dBsleV+Se2h2ID/S81Knt9EYOgC4ped57/c5Tt/1XHH+VJLdgJcBly1SXLOZL9YPAX8M3LuYQc1iu3EmeRzNGYzfbIcuX5JkzxHECPN/n68HPgP8L+CCqrp6EWNbiHH6e5o0feWHrtrO/+2dkyTAJ4FTRh3LAB1Cc/b/4jZXntX2s7OqajPN2dFvAFcCx1fVT+Z8U5+6Vrwto6lgp20DHhpRLANTVW+pqgNpjh6+JclLRh1Tv5LsBZwPvKnnKElnt9N2+tPZbVRVH6uqPYEPAp/vSYad3UZjaq7vc5y+63ljaa8f+nPgnKrauHihPcJ2Y01yKPCsqrpwFIHNMNd3uhfwczRnMH6Rpuj4nUWN7mfm2/ZvAT4FnAy8coyv6x2nv6dJM7Hf7fb+b++otwFTVTXSa30H7MnAwcC7gOcAzwNeMdKIHqUkTwCOpymy/xB476CuI+9a8XY7zVjsafvTnAKfCFV1G821Ec8cdSz9SPJzNPF+sKqu6nmpk9tpjv78VNe20bSq+mua60imj/p3chuNsbm+z3H6rueMpS3uzwWur6o/XuTYZpor1jcCT09yDc01Hwck+cwixzdtrjjvBrZU1fp2h/FS4BcWOb5p240zycHA4VX1X6vqa8BngcFdXD9Y4/T3NGkm8rvt5//2jnkD8Jo2/30EODbJb484pkfr+8DVVbWpmhvNr2d0uXJQXg/8Y1VNVdX/aNuOHMSKO1W8VdWtwANJ1rQzYb0B+KsRh/WoJXl6+++eNGd2xj65tEcULgNOr6rP9b7Wxe00V3/a17u4jZ7ajiMnya8BD7Zj/ju5jcbcF4Cjkjy5Z+z+F2Dsvuvtxtn6FHBHVX1oJNE93Fzf6Xur6heq6jDgXwO3VdVvjGGcPwH+V5Lpa7Jezuhyx1zb/v8AP59kRXvm9dnAD0YU55zG7O9p0syXHzpnvv/bu6iqnltVh7b570PAJVX18VHH9Sh9Azg4yb5JdgZeAnxzxDE9Wg8ChyV5XJLdgYMYVF6dbRaTUT5oZju6iWZGoPvan48F3tu+/hxgA83RoD8YdbwD6tPf0MxIcwPwzlHH22effhd4oO3L9OO0rm6nPvrTxW30HOBG4LvA14DDu/63NM4P4IT2u/5u+z2P5Xe9vTiB59MMk+r9G3jtOMY6Y5lVjHC2yT62/VOBv2+/z79kQLONDSHO99IM6/wO8BfAbiOMc+L2A7rymPk7Mup4BtCf2f5vf+qo4xrw9pqU2SaPAa5rt9HvjjqeAfRnGXBBm6duBH5nUOtO+wGSJEmSpDHWqWGTkiRJkrRUWbxJkiRJUgdYvEmSJElSB1i8LUFJfjHJ5Ul+kORHSb4y4PX/fZKXDXid1d5gU5IkSVqSBnKzOHVHOwXrF4CPA79Bc++vlw7yM6rqeYNcnyRJkiScbXKpSfIc4O+ratdHsY7HVNW2AYbVz2cWcGhVXbuYnytJkiSNC4dNLj2bgZ2SfDBJphvbG57e3btgkruTrGl/nkryn5P8I/DFJDcnOaFn2Rcmuau9GeHGJC9P8sYk35uxzi8nObX9+RVJrkvywySfSfKknuXe3a7nh0k+MIwvQpIkSeoSi7clpqruBN4O/D5wVZJfWcDbXwscTzPc8tM0N02d9irg01X1k562S4CVSZ4NkGQF8GvAXyQ5DLgQeBfNTXZ3Bs5ol3sl8IH281YB/2pBnZQkSZImkMXbElRV5wG/BNwFfC3Ju/p862er6tqq+j7wl8BLkyxP8hjglcCfz/icfwb+hp8VeccBX2wLyLcC51bVl6rqh8DZwCva5U4E/qiqvt6+9ts73FlJkiRpQli8LVFVdUNVHQO8E/hD4Of7eNvGnvdvAL4LHA08H/hhVV01y3v+kp8Vb6/iZwXegcCp7SySBfwdsG/Pazf2fNa9fXZLkiRJmlgWb0tcVX0K2EJTmC2fbk+yE7D7jMVnTlIyXZi9Cjh/Ox9xBXBgkucBzwIubdvvAP6gqtLz2Kl97V7ggJ5YnrLQfkmSJEmTxuJtiUmyOskpSVYm2SXJa2mKso3AtiQvbxd9O7BsntV9Gngx8BLggtkWqKofA5+luZ7tkqp6sH3pL4CTkjwvya5JfinJke1rlwLvTnJokr2BjwJOiypJkqQlzeJt6bkXeDVwAzB97dnLq+pW4LeATyW5HtgTuGeuFVXVd4Fbgdvb92/PXwK/Ts81cVV1JfDhtu1uYB3wUPvyH9FcK/cV4GrgMzzyrJ8kSZK0pHifN0mSJEnqAM+8SZIkSVIHWLxJkiRJUgdYvEmSJElSB1i8SZIkSVIHWLxJkiRJUgc8dtQB7LXXXrVq1ap5l3vggQdYvnz5vMuNg67Eapz/f3v3H2t3Xd9x/PlaWcdPfyB4C22lCsEJMnG9Sxwp4zYbAnNZAsuUgQ7msG4hgSnOEY1uf7AGzUjMajSpYroJEWu22mjnFNTrzH6oVHAFFgjDQksKAl0Il4Fbe9/74xzs4a6959zyPffeb87zkZz0ns/308/3dc7tSfrK93POaVZbcsLgWbdv3/5kVZ04D5EkSZLUx4KXt1WrVnHnnXf2nTc5OcnExMTwAzWgLVnN2ay25ITBsyZ5ePhpJEmSNAi3TUqSJElSC1jeJEmSJKkFLG+SJEmS1AKWN0mSJElqgQX/wJJB7Xj0aa68flvj6+688W2NrylJkiRJTfPKmyRJkiS1gOVNkiRJklrA8iZJkiRJLWB5kyRJkqQWsLxJkiRJUgtY3iRJkiSpBQYqb0l2Jnmwe/tud+zaJI8kuT/JRT1zb0yyO8mOJKuHFVySJEmSRsnA3/NWVae98HOSU4GrgTOBlcAdSU4BzgXWAKuAtcDNwNkN5pUkSZKkkXS42yYvBjZX1TNVdR+wE1gNXAJsqqp9VXU7cGKSZc1ElSRJkqTRNeiVt+eS/CfwBPDndK623dNzfDdwUnd8a8/4o93xx3oXS7IOWAcwNjbG5ORk3wBjR8F1Z+0bMO7gBjn3XE1NTQ1l3aaZs1ltyQntyipJkqSOgcpbVb0BIMm5wBbgS8B0z5RpYD+w9BDjM9fbCGwEGB8fr4mJib4ZNty6lZt2DLzLc2A7L+9/7rmanJxkkMe00MzZrLbkhHZllSRJUsectk1W1XfpbJHcAyzvObQC2HWQ8ZPpXJWTJEmSJL0EfctbkmOSnNT9+c10tkF+E7g0ydFJzgCOB+4GtgFXJFmS5HzggaraO7z4kiRJkjQaBtmHeDTwnSRLgKeBd1bVPye5BbgXeB64qqoqyRbgPOAh4CngsiHlliRJkqSR0re8VdUTwOkHGV8PrJ8xNg1c071JkiRJkhpyuF8VIEmSJEmaR5Y3SZIkSWoBy5skSZIktYDlTZIkSZJawPImSZIkSS1geZMkSZKkFrC8SZIkSVILWN4kSZIkqQUsb5IkSZLUApY3SZIkSWoBy5skSZIktYDlTZIkSZJaYKDylmRpkvuSfLZ7/9okjyS5P8lFPfNuTLI7yY4kq4cVWpIkSZJGzREDzvsQsBMgyanA1cCZwErgjiSnAOcCa4BVwFrgZuDsZuNKkiRJ0mjqe+UtyRuAXwE2d4cuBjZX1TNVdR+dUrcauATYVFX7qup24MQky4YTW5IkSZJGS6rq0AeTAN8A/pjOVbU1wLPAPVX1me6czcAXgCuBT3aLG0m+D7y3qu46yLrrgHUAY2Njq2+77ba+QX+y92kef24uD20wZy1/eeNrTk1Nceyxxza+btPM2ay25ITBs65du3Z7VY3PQyRJkiT10W/b5B8Bk1X1YJI13bGlwHTPnGlg/yzj/09VbQQ2AoyPj9fExETfoBtu3cpNOwbd5Tm4nZf3P/dcTU5OMshjWmjmbFZbckK7skqSJKmjXxt6F3Bckt8FjgeOAf4aWN4zZwWwC9gzY/xkYHdzUSVJkiRpdM36nreqOqeqzqqqs4GPAluArwKXJjk6yRl0St3dwDbgiiRLkpwPPFBVe4ecX5IkSZJGwpz3IVbV9iS3APcCzwNXVVUl2QKcBzwEPAVc1mhSSZIkSRphA5e3qtoEbOr+vB5YP+P4NHBN9yZJkiRJatBAX9ItSZIkSVpYljdJkiRJagHLmyRJkiS1gOVNkiRJklrA8iZJkiRJLWB5kyRJkqQWmPP3vEmaX6uu39b4mpsuPKbxNSVJkjRcXnmTJEmSpBawvEmSJElSC1jeJEmSJKkFLG+SJEmS1AKWN0mSJElqgb7lLcnPJbk9yQNJ7k9yQXf82iSPdMcu6pl/Y5LdSXYkWT3M8JIkSZI0Kgb5qoACfr+q9iS5EPjLJA8CVwNnAiuBO5KcApwLrAFWAWuBm4GzhxFckiRJkkZJ3ytv1bGne/cU4EfAxcDmqnqmqu4DdgKrgUuATVW1r6puB05Msmw40SVJkiRpdKSq+k9KPgj8GfAEcAHwfuCeqvpM9/hm4AvAlcAnu8WNJN8H3ltVd81Ybx2wDmBsbGz1bbfd1jfDT/Y+zePPDfy4BnbW8pc3vubU1BTHHnts4+s2zZzNGlbOHY8+3fiar335kjyPGJQAAApZSURBVIGyrl27dntVjTceQJIkSXM2yLZJqurjwMeTXAJ8Hfg2MN0zZRrYDyw9xPjM9TYCGwHGx8drYmKib4YNt27lph0DxZ2TnZf3P/dcTU5OMshjWmjmbNawcl55/bbG19x04TGteE4lSZJ0wJw+bbKq/h44FtgDLO85tALYdZDxk4HdLzGjJEmSJI28QT5t8nUvvG8tya8CzwPbgEuTHJ3kDOB44O7u+BVJliQ5H3igqvYOL74kSZIkjYZB9iG+AvjHJEuAx4F3VNX2JLcA99Ipc1dVVSXZApwHPAQ8BVw2pNySJEmSNFL6lreq+iFw+kHG1wPrZ4xNA9d0b5IkSZKkhszpPW+SJEmSpIVheZMkSZKkFrC8SZIkSVILWN4kSZIkqQUsb5IkSZLUApY3SZIkSWoBy5skSZIktYDlTZIkSZJawPImSZIkSS1geZMkSZKkFrC8SZIkSVILWN4kSZIkqQX6lrckRybZmOSBJA8neV93/NokjyS5P8lFPfNvTLI7yY4kq4cZXpIkSZJGxREDzDkG+DrwXuBVwL1JfghcDZwJrATuSHIKcC6wBlgFrAVuBs5uPrYkSZIkjZa+V96q6qmq+rvqeBLYBfwasLmqnqmq+4CdwGrgEmBTVe2rqtuBE5MsG2J+SZIkSRoJg1x5+5kkbwSOBE4A7uk5tBs4ic5VuK094492xx+bsc46YB3A2NgYk5OTfc89dhRcd9a+ucQdyCDnnqupqamhrNs0czZrWDmH8e++Lc+pJEmSDhi4vCU5Afg88AfAu4HpnsPTwH5g6SHGX6SqNgIbAcbHx2tiYqLv+TfcupWbdsypaw5k5+X9zz1Xk5OTDPKYFpo5mzWsnFdev63xNTddeEwrnlNJkiQdMNCnTSZ5JfBV4ENV9QNgD7C8Z8oKOtspZ46fTOeqnCRJkiTpJRjk0yZfBnwFuKGqvtYd3gZcmuToJGcAxwN3d8evSLIkyfnAA1W1d0jZJUmSJGlkDLIP8RrgzcAnknyiO/ZW4BbgXuB54KqqqiRbgPOAh4CngMuajyxJkiRJo6dveauqG4AbDnJofffWO3eaTtm7ppF0kiRJkiRgwPe8SZIkSZIWluVNkiRJklrA8iZJkiRJLWB5kyRJkqQWsLxJkiRJUgtY3iRJkiSpBSxvkiRJktQCljdJkiRJagHLmyRJkiS1gOVNkiRJklrA8iZJkiRJLTBweUtyVJLThxlGkiRJknRwfctbkpcl+TLwOPDBnvFrkzyS5P4kF/WM35hkd5IdSVYPJ7YkSZIkjZYjBpgzDWwAvgq8BSDJqcDVwJnASuCOJKcA5wJrgFXAWuBm4OzGU0uSJEnSiOl75a2qpqrqm8C+nuGLgc1V9UxV3QfsBFYDlwCbqmpfVd0OnJhk2RByS5IkSdJIGeTK28GsBO7pub8bOKk7vrVn/NHu+GO9fznJOmAdwNjYGJOTk31POHYUXHfWvr7z5mqQc8/V1NTUUNZtmjmbNaycw/h335bnVJIkSQccbnlbSmc75Qumgf2zjL9IVW0ENgKMj4/XxMRE3xNuuHUrN+043LiHtvPy/ueeq8nJSQZ5TAvNnM0aVs4rr9/W+JqbLjymFc+pJEmSDjjcrwrYAyzvub8C2HWQ8ZPpXJWTJEmSJL0Eh1vetgGXJjk6yRnA8cDd3fErkixJcj7wQFXtbSirJEmSJI2svvsQkxwH3AUcBxyZZAJ4D3ALcC/wPHBVVVWSLcB5wEPAU8BlQ8otSZIkSSOlb3mrqmeA0w5y6NvA+hlzp4FrujdJkiRJUkMOd9ukJEmSJGkeWd4kSZIkqQUsb5IkSZLUApY3SZIkSWoBy5skSZIktYDlTZIkSZJawPImSZIkSS1geZMkSZKkFrC8SZIkSVILWN4kSZIkqQUsb5IkSZLUApY3SZIkSWqBxstbkrcn+XGSB5O8u+n1JUmSJGkUHdHkYkmOA24C3gLsB+5O8pWqeqLJ80iSJEnSqGn6ytsFwHeq6tGqegz4FvDrDZ9DkiRJkkZOo1fegJXAwz33dwMnzZyUZB2wrnt3Ksn9A6x9AvDkS044M8vHml4RGFLWITBns9qSk7UfGzjrKcPOIkmSpME0Xd6WAtM996fpbJ98karaCGycy8JJ7qyq8ZcWb360Jas5m9WWnNCurJIkSepoetvkHmB5z/0VwK6GzyFJkiRJI6fp8vYN4IIkr06yDDinOyZJkiRJegka3TZZVY8l+TDwr92h66rq2YaWn9M2ywXWlqzmbFZbckK7skqSJAlIVS10BkmSJElSH41/SbckSZIkqXmWN2kEJDkqyekLnUOSJEmHb1GWtyRvT/LjJA8mefeMY29M8qMkDyfZkGTBHkOfnNcm+Y9uzs8nafprGRrJ2TPn5iQPzne2g+SYNWuSv0iyK8nOJOcsRMZujtl+97+RZEf3+OeSLFnAnC9L8mXgceCDBzm+aF5PkiRJmt2i+49akuOAm4A13dv6JCf2TPkUcD3wOuCXgN+e95AMlPMZ4E3AqcAY8I55D8lAOUmyFli2APFepF/WbkkaB04HXgv8cDHmBD4D/B5wGp2sb533kAdMAxuA9x/i+KJ4PUmSJKm/RVfegAuA71TVo1X1GPAt4NcBuv9Bfm1Vfa2q9gO3AhcutpwAVfW5qvqfqtoH/Dtw/GLMmeRI4AbgIwuUr9esWYH3AX9SVc9Vx/MLkrJ/zp/2/PwLwE/mM1yvqpqqqm8C+2YeW2SvJ0mSJPWxGMvbSuDhnvu7gZO6P68AHjnEsfk2W86fSXI08DbgK/OUa6Z+OT9K5+rL3vkMdQiHzJrk5+lcHfzDJPcn2ZLkVQuQEfo/p+8Evgh8D7ilqrbPY7a5WEyvJ0mSJPWxGMvbUjpbvV4wDewf4Nh865ul+/6hvwU2VNXO+Yv2IofMmeQs4E1VdetCBDuI2Z7TE4BX0rnK9Yt0SseH5zXdAf1+9+8BPg1cA/xOktfMY7a5WEyvJ0mSJPWxGMvbHmB5z/0VwK4Bjs23WbMkCfBZ4L6q+tQ8Z+s1W84rgNOS3A38A7AyyRfnOV+v2bI+CUxV1e3V+XLCrcDr5znfCw6ZM8kZwOqq+mRV/QvwZeDq+Y84kMX0epIkSVIfi7G8fQO4IMmrkywDzumOUVWPAM8mmeh+gt+7gC8ttpxdnwYeq6qPLki6A2Z7Pj9QVa+vqrOB3wR2VdWCfLDKAFn/F/hekhfek/VbwA8WJuasv/ufAq9JMta98vpm4L8WKOesFtnrSZIkSX2kcxFjcUlyJQc+QOMD3T9Praq/SvLLwN8ArwA2VdWCfdDGoXIC/wb8E/BQz/SPVNUX5i/dAbM9nz1zVgF3VNVp8xpuhj6/+9cBn6fz6Z0/AK6qqmfnP2XfnB+gs2XypxzI+d/zn/Jnn4x5F3AccCTwBPCnLMLXkyRJkma3KMubJEmSJOnFFuO2SUmSJEnSDJY3SZIkSWoBy5skSZIktYDlTZIkSZJawPImSZIkSS1geZMkSZKkFrC8SZIkSVILWN4kSZIkqQUsb5IkSZLUAv8Hl8SVrgZ10ZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = train[[\"Survived\",\"Sex\", \"Pclass\", \"Age\", \"Fare\", \"SibSp\", \"Parch\", \"Embarked\"]]\n",
    "pd.DataFrame.hist(df2, figsize = [15,8]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:30:24.974222Z",
     "start_time": "2019-06-17T07:30:24.425555Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nz9ahQ4c0ffr00KFhGKxatUodHR0aO3aszExjx45VR0eHVq1aFTq0vEHyBgAAgFjoStx2796tyZMna/fu3d0JHOKvq35ffvll7dixQy+//DL1exIGLAEAAEBs/PCHPzztNOKN+j09BiwBAABAbFxyySWnnUa8Ub+nR7dJAAAAxMK0adPU2tqqOXPm6Pnnn9ecOXPU2tqqadOmhQ4Nw4D67R+jTQIAACAWDh48KDNTa2urWltbe5Qj/g4ePKjRo0f3qN/i4mLqNwstbwAAAIgFM+v++8Ybb+y1HPE1a9Ystbe3a/Hixbrzzju1ePFitbe3a9asWaFDyxskbwAAAIgVd1dNTY3cPXQoGEaPPPKIFi9erLvuuktveMMbdNddd2nx4sV65JFHQoeWNxhtEgAAALHBaITJ1tjYeNrpQsdokwAAAIgNRiNMtrq6utNOFzq6TQIAACBWzEypVIp73RKmqqpKzc3NWrJkiQ4fPqwlS5aoublZVVVVoUPLG4w2CQAAgFhw9+6E7Utf+lKPcsTfnj17NGvWLDU3N6u5uVlSJqHbs2dP4MjyBy1vAAAAiA13l7urpaWl+28kx549e3rUL4lbT7S8AQAAIDZ66ypJApcc06dP16FDh7qnp02bxnPestDyBgAAgFjITtwaGhp6LUd8dSVus2fP1ubNmzV79mwdOnRI06dPDx1a3iB5AwAAQKy4u2bPnk2LW8J0JW67d+/W5MmTtXv37u4EDhnBuk2a2SJJiyoqKkKFACCGqm4LOOLUbWE2+8jlPJy0P0O96m43D35dTh6B3Lr77rtPmb744osDRYPh1ttz/N70pjcFiib/BEve3H2LpC3V1dXLQsUAIH6Otq3RgTUX5Xy7qVRKNTU1Od9u+eqtOd9mHA0lgQpVtwAG5+KLL+6xz5O4Jcsll1yi3bt395jGn9BtEgAAALFiZmptbeVet4SZNm2aWltbNWfOHD3//POaM2eOWltbNW3atNCh5Q1GmwQAAEAsZD/nrb6+vkc54u/gwYOaPn26Wltb1draKonRJk9GyxsAAABig+e8JdvBgwd71C+JW08kbwAAAAAQAyRvAAAAABADJG8AAAAAEAMkbwAAAAAQA4w2CQAoSEVFRers7OyeHjVqlDo6OgJGBADo7fEPDErzJ7S8AQAKTlfiVlpaqvXr16u0tFSdnZ0qKioKHRoAFKzsxG3KlCm9lhc6kjcAQMHpStyOHj2q8847T0ePHu1O4AAAYbm77rjjDlrcehGs26SZLZK0qKKiIlQIAGKqfPXWMBv+ae63WzZ2dM63WSh27tx5yvQFF1wQKBqgMIVsUSExyE8n5wYVFRXat29foGjyT7Dkzd23SNpSXV29LFQMAOLnwJqLgmy3fPXWYNvGyJg3b56OHj3aYxpAbg0lgeJ7OZlOTtRI3Hqi2yQAoOCMGjVK6XRa48aN0+OPP65x48YpnU5r1CgOiwAQmpnp4x//OPe69YLRJgEABaejo0NFRUVKp9O6+uqrJTHaJACE5u7dCdtzzz3XoxwZXGIEABSkjo4OubtaWlrk7iRuAJAH3L3HdzOJW08kbwAAAAAQAyRvAAAAABADJG8AAAAAEAMkbwAAAAAQAyRvAAAAABADJG8AAAAAEAMkbwAAAAAQAyRvAAAAABADJG8AAAAAEAMkbwAAAAAQAwNO3sxsrJm9vZ9lzjOzJjP7sZldMPTwAAAAAACSVNzfAmY2XtI/S7pQ0g8kfToq/5ikmyV1SPqqu/+jpM9K+oykTklrJV0xIlEDADBEpaWlOnbsWPd0SUmJ0ul0wIgAAGZ2Spm7B4gkPw2k5a1T0jpJK7sKzGycMsnZ3Ojnq2Z2tqSz3f2P7v7vksaPQLwAAAxZV+JWXl6u22+/XeXl5Tp27JhKS0tDhwYABau3xO105YWo3+TN3dPuvl1Se1bxQkk73f1pd39G0g5J8yUdNbOJZjZB0h9HJGIAAIbo2LFjmjx5skpKSnT55ZerpKREkydP7tESBwAIw93V0tJCi1sv+u022Ydpkp7Mmn5K0hslfUvSRkknJH25r5XNbLmk5ZI0depUpVKpQYYxeH+z/ZiOvTa4dZ+8+eLhDeYMnLvq7jNep2S09A/zS0YgmvxUSHUrFV79DkVtbe2Q1rebB79uS0vLkLaN4efuqqur04wZM7R//341NDRIUpBjEkYWdTryhnLsHYry1Vtzvk2OuyMvlUopnU732HfZjzNsoBmtmV0haa67f9rMVkkqdfe/i+atkfR7d//2mQZQXV3tDz744JmuNmTlq7fqwJqLcr7dVCqlmpqanG4z1HsNpZDqViq8+g0lVP1iZJiZpk6dqmeeeaa7bs855xw9++yzXOlNGL4jcyPE/5njbvJ0dY909+76zS5LMjP7lbtX97fcYFve/iCpJmv6LZJ+PsjXAgAg55599lnuowCAPMR3c98G+5y3bZIWmtkUMztH0uyobMDMbJGZbTxy5MggQwAAYHAmTpx4RuUAgJHXV+ta0lvdzkS/yZuZjTOzfco8FuDS6O9KSfWSfiZpt6Tr3P2M7vJ29y3uvrysrGwQYQMAMHgvvfSSJk6cqB07dui+++7Tjh07NHHiRL300kuhQwOAgubuPQYsIXHrqd9uk+5+VFJFH7NvHdZoAADIgfb2dr3rXe/S/Pnz5e4yM9XW1mrHjh2hQwMAoE+DvedtyMxskaRFFRV95YUAAIyMoqIipVIpff3rX9f555+vvXv36vrrr1dRUVHo0AAA6NNg73kbMrpNAgBC4b4KAEAcBWt5AwAglM7OTl144YX63Oc+R7dJAEBskLwBAApOcXGxHn74YW3fvl0dHR0qKirSJZdcouJiDosAgPwVrNskjwoAAIQyfvx4HT58WA899JDa29v10EMP6fDhwxo/fnzo0AAA6FOwS4zuvkXSlurq6mWhYgAAFKbDhw/ryiuv1Be+8AW98sorGjNmjK688kp997vfDR0aAAB9CtbyBgBAKJWVlbr00kt14sQJtbS06MSJE7r00ktVWVkZOjQAAPpE8gYAKDj19fWqq6tTS0uL2tvb1dLSorq6OtXX14cODQCAPvGcNwBAwVm6dKkkacWKFWpra1NlZaUaGhq6ywEAyEc85w0AUJCWLl2qRx99VNu3b9ejjz5K4gYAyHt0mwQAAACAGCB5AwAAAIAYIHkDAAAAgBjgId0AAAAAEAMMWAIAAAAAMUC3SQBAQWpqatLMmTM1f/58zZw5U01NTaFDAgDgtII95w0AgFCamppUX1+vxsZGdXR0qKioSHV1dZLEIwMAAHmLljcAQMFpaGhQY2OjamtrVVxcrNraWjU2NqqhoSF0aAAA9InkDQBQcNra2jR37tweZXPnzlVbW1ugiAAA6B+jTQIACk5lZaV27drVo2zXrl2qrKwMFBEAAP1jtEkAQMGpr69XXV2dWlpa1N7erpaWFtXV1am+vj50aAAA9IkBSwAABadrUJIVK1aora1NlZWVamhoYLASAEBeI3kDABSkpUuXaunSpUqlUqqpqQkdDgAA/WLAEgAAAACIAZI3AAAAAIgBkjcAAAAAiAGSNwAAAACIAZ7zBgAAAAAxYO4eNgCzP0p6MmgQuTVZ0vOhg8CIoG6TjfpNLuo22ajf5KJuk63Q6vdcdz+7v4WCJ2+FxswedPfq0HFg+FG3yUb9Jhd1m2zUb3JRt8lG/faOe94AAAAAIAZI3gAAAAAgBkjecm9j6AAwYqjbZKN+k4u6TTbqN7mo22SjfnvBPW8AAAAAEAO0vAEAAABADJC85ZiZjTWzZjMb0v/ezG41s08MV1wATmVm7aFjAIBCZmYXmdlno78590E3M/uimf1t6DhyjeRtAMzsv5vZY2Z2wMx+PZTXcvfj7r7Y3TuHKz7k1nB+HpB7ZuZmts/MnjSzO81sYuiYMPLMbFZU97NDx4Lhk7U/7zOzfw0dD4bu5GOsu29192/2s46Z2VfNrM3MDpnZXbmKFwPH8Xd4FIcOIN9FB/rLJFW7+3EzKx/geubcUJg4g/08IK90uHuFmZmkb0n6W0krA8eEkfdJSa2SPhH9RjJ0uHvFmazA8Tl/DeEYu1RSpaQqd2/n2Jy3BnX8ZZ/tiZa3/k2U1CmpXZLc/cDJzbTR1aG3mFl5dEXh+5Luj67+vC1aZmp0pWGUmbVHV4n6mr/AzB42s/9nZl+O5heZ2bfNbL+ZpSS9Mcf/B2T09nkYZWZfj674/cbMZptZhZkdNLPXm9kEM/udmU0OGzqyRQeCeyWVS5KZvdfMfmFmT5jZbdnLmlmJmd0T1eOjZlYVlddFyx80sw+a2WQzuz/al+/L+ZtCr6Ju6h+TdJWkj5rZ6Kh8eVR/vzazTWb2vah8kpndZWa/NbPtZnZOwPBxhszsr83s8Wg//GZU1uP4HJVdb2Z7o+/uDwcNGl16O8Ze0bVvRmaa2c/N7Ckzuy5rvY7oR+5+QOruVvcPZvYv0TnXN3L2TtCnXo6//zOrRW5pVHZF1Dr3gKQGMzvLzL4ZnRsfMLN3RS833cxSZvasmV0f5A3lGMlb/+6V9HtJD5nZBwewfLmk70p6v6TNkpZE5R+RdEdXd8nog3vKfElvkPQVSTXKXEWqNbN3S/pvkt4q6T9I+mtJVUN8Xxic3j4PVyhzNalS0sclrXf3fcrU51WSPi9prbs/HyBe9MHMxirTCnO/mY1XZn/8G3d/q6TPnby4pHp3f5ukDZK6Thi+IWm2Mvv9A8q07uxz93OV+SwgP1wo6Xfu/oikNkkfMrPzJNVLep+k9yo6iYh8U9IGd3+HMp+LG3IbLs5AUZSoPW5m34nK9kt6p6R3SLo0qxWmXNHx2cwulPRuZY6lfyHp2zbEe9ExLAZyzvVeSfMk/SdJK83srZJul3SOpF1m9mcnLX+hMuda50laYGY1IxE4Bi77+BsV3R21oF8k6aasRd8bldUrc9x9o6TzJb1d0hPRMn8m6WJJcyR90cwS36sw8W9wqNz9NUl/ZWaLJX0z+v3caVZ53t3/RZLM7A5JX1PmBO+jOvWEsLf5s5U54DwQLVMq6W2SFkra6O7tkp4xsx3D8PZwhvr4PEyR9B4z60rEy6LfX5a0W9IRZU4OkB+KzOxxSa9I+r+S1iuzf/3K3X8pSe7+x+wV3D1tZuea2ackvUfSS9Gsf5X0HUk3uvtjZvZLSZ81s+WSbs3Ju8FAfEKZJEzR709ISkn6sbs/K0lm9gNlTgIk6UOS/nPUalMs6eGcRosz0eHu551U9rQyJ3pVyhxD3xyVZR+f/1KZi6SPReuMkTRV0h9yEDP60Mcx9hcnLfZ9dz8h6UR0LvRud/+Rmf25pMsl/dDMvuPuN0fL3+nuL0qSmf1EUrUy+z9yr7fjryQdN7M1yjRavDlr+Z3u/u+SFJ1jXRV9RiTpVTOTpC3unpa0z8wOSzpbCd+PSd4GyN2boy+JvZK+J6koa/borL/TWev83MzebGbvkDTZ3X9z0mueMt/MZkja7u4fzV7WzC6X9FpW0ZjheWcYjJM+D49Lutbdt5y8WPTDfpZfTjnZM7PXKeqm0xszu0rShyWtkrRN0meiWYuVudei2cxWu/tmM5unzFXCq82s2t07RuJNYGCiK7wfkfSXZnajMj1OSiU9KunVrEVHn/T3e9z9aM4CxbCIrrq3KtPj4VZJ05VpOZeyjs/KfC/f5O7rchogBuSkY+yjJ83O/q4eK+l4tI5LutXMfirp38ysKzF47aTlE31in+d6O/7+R2UaM65QpjHj91mzs/fZvo7TJ7L+fk09z88TiS4C/TCzqqwuF6bMwf6ApHdF8yuVaarvy48krVXmgzmQ+b+UNM/MKqLXr4nKH5B0hWXur3qbMt0ykWN9fB52SVpmZsVmNtrM3hfN/4oyiX6bpE/nOlackQckzY32Z5nZm06aP1NSS3QBZmFWeYW7f1+ZG6/nRN13DirTXfZNksaPeOToz4clPeDuU9z9HHefokyLuJS5/22Cmb1emda4LrskXSNJZjbFzM7PbcgYgjcok5z/RJmTvVl9LLdL0n81s1LLoHdEHujjGHvy/eIfscw4ADOU6Sr3C8vcszwla72X9aeT+ossc//5JEl/JYmeS/mlUtJvo1bxmtMst13SZ6KDUL7VAAABr0lEQVTz4LFmNiEn0eUhkrf+TZB0n5ntV+Zq3hplut283sy2K3NSfrqrOHdI+qCk/zOQ+e7+tKTPRtv8naRro+W+rUxr25OS1kliMIQwevs8fE2ZrpH7lblC+DYzu0CZel0v6e8l/b0xJG7ecvdnlNmXfxztd58/aZF/krTczB5TprtHl81mtk+ZwTC+LenPldlHH5P0v7q66iCoT0r63yeV/aMyJwmbldlnU8p0zepqJV0h6WIzO6DMPThn5SBODIPo3uLNkvZJ+rpObbXp8iNlErjHJf2b/tRlFmH1dow9eNIyTytzoft+SSuiOj9XmSTud5K2Slru7l0t67+V1BKts9bdHx/5t4EzsE1SmZk9ocz9bH35oqQSZc61fqnTN5wkmjHyJgCg0JnZ3ylzTPwfoWMBMDzM7IuS2t39K6FjAYYLLW8AgIJjZuOjFnKZ2VuUGdH3nrBRAQBwegykAAAoREWSvmdmZytzf8yartFGAQDIV3SbBAAAAIAYoNskAAAAAMQAyRsAAAAAxADJGwAAAADEAMkbAAAAAMQAyRsAAAAAxADJGwAAAADEwP8HNM5OB6+dMyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,4))\n",
    "df2.boxplot(return_type='dict')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:40:15.688484Z",
     "start_time": "2019-06-17T07:40:13.854987Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 589.25x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# conda install seaborn\n",
    "import seaborn \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "df = train[[\"Survived\", \"Age\", \"Fare\"]]\n",
    "seaborn.pairplot(df, hue = \"Survived\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:42.027862Z",
     "start_time": "2019-06-17T07:22:41.477649Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['fare'] = [i.left for i in pd.cut(train['Fare'], 8).tolist()]\n",
    "train['age'] = [i.left for i in pd.cut(train['Age'], 8).tolist()]\n",
    "\n",
    "seaborn.lineplot(x=\"age\", y=\"fare\", hue=\"Survived\", #style=\"Survived\", \n",
    "                 data=train);\n",
    "#plt.xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T14:40:37.654631Z",
     "start_time": "2019-06-17T14:40:36.882611Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1238.64x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.relplot(x=\"Age\", y=\"Fare\", \n",
    "                col=\"Sex\", \n",
    "                size=\"Pclass\", \n",
    "                alpha=.9,\n",
    "            height=4, aspect = 2, data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T09:33:30.182342Z",
     "start_time": "2019-06-17T09:33:29.892541Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the residuals after fitting a linear model\n",
    "seaborn.residplot('Fare', 'Survived', \n",
    "                  lowess=True, color=\"r\", data = train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T09:35:05.130328Z",
     "start_time": "2019-06-17T09:35:04.875724Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the residuals after fitting a linear model\n",
    "seaborn.residplot('Age', 'Survived', \n",
    "                  lowess=True, color=\"r\", data = train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T09:31:26.863447Z",
     "start_time": "2019-06-17T09:31:26.458106Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = seaborn.cubehelix_palette(rot=-.2, as_cmap=True)\n",
    "ax = seaborn.scatterplot(x=\"age\", y=\"fare\",\n",
    "                     hue=\"Survived\", size=\"Pclass\",\n",
    "                     #palette=cmap, \n",
    "                     sizes=(10, 200),\n",
    "                     data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T14:14:00.241782Z",
     "start_time": "2019-06-17T14:13:59.135289Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up a grid to plot survival probability against several variables\n",
    "seaborn.set(font_scale=1.2)\n",
    "\n",
    "g = seaborn.PairGrid(train, y_vars=\"Survived\",\n",
    "                 x_vars=[ \"Sex\",\"Pclass\", \"fare\", \"age\"],\n",
    "                 height=4, aspect=.8)\n",
    "\n",
    "# Draw a seaborn pointplot onto each Axes\n",
    "g.map(seaborn.pointplot, scale=1.3, errwidth=4, color=\"xkcd:plum\")\n",
    "g.set(ylim=(0, 1))\n",
    "seaborn.despine(fig=g.fig, left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:27:19.131929Z",
     "start_time": "2019-06-17T07:27:18.860668Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = seaborn.catplot(x=\"Sex\", y=\"Survived\", data=train,\n",
    "                height=4, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"Survival probability\", fontsize = 20)\n",
    "g.set_xlabels(\"Sex\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:42.497462Z",
     "start_time": "2019-06-17T07:22:42.030002Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 329.875x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = seaborn.catplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=train,\n",
    "                height=4, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"Survival probability\", fontsize = 20)\n",
    "g.set_xlabels(\"Class\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:42.941310Z",
     "start_time": "2019-06-17T07:22:42.500037Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 473.875x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a pointplot to show pulse as a function of three categorical factors\n",
    "g = seaborn.catplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", #col=\"diet\",\n",
    "                capsize=.2, palette=\"YlGnBu_d\", height=3, aspect=2,\n",
    "                kind=\"point\", data=train)\n",
    "g.despine(left=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:44.979712Z",
     "start_time": "2019-06-17T07:22:42.943501Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1013.88x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a pointplot to show pulse as a function of three categorical factors\n",
    "g = seaborn.catplot(x=\"age\", y=\"Survived\", hue=\"Sex\", col=\"Pclass\",\n",
    "                capsize=.2, palette=\"YlGnBu_d\", height=3, aspect=1.5,\n",
    "                kind=\"point\", data=train)\n",
    "g.despine(left=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:46.440944Z",
     "start_time": "2019-06-17T07:22:44.982692Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1013.88x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a pointplot to show pulse as a function of three categorical factors\n",
    "g = seaborn.catplot(x=\"fare\", y=\"Survived\", hue=\"Sex\", col=\"Pclass\",\n",
    "                capsize=.2, palette=\"YlGnBu_d\", height=3, aspect=1.5,\n",
    "                kind=\"point\", data=train)\n",
    "g.despine(left=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:47.234304Z",
     "start_time": "2019-06-17T07:22:46.443556Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 905.875x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = seaborn.catplot(x=\"age\", y=\"Survived\", hue=\"Sex\", data=train,\n",
    "                height=4, aspect = 3, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"Survival probability\", fontsize = 20)\n",
    "g.set_xlabels(\"Age\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:48.063045Z",
     "start_time": "2019-06-17T07:22:47.236677Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.875x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = seaborn.catplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", col =\"Child\" , data=train,\n",
    "                height=4, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"Survival probability\", fontsize = 20)\n",
    "g.set_xlabels(\"Class\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.733932Z",
     "start_time": "2019-06-17T07:22:48.066027Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 643.75x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = seaborn.catplot(x=\"Pclass\", y=\"Survived\", hue=\"age\", col =\"Sex\" , data=train,\n",
    "                height=4, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"Survival probability\", fontsize = 20)\n",
    "g.set_xlabels(\"Class\", fontsize = 20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Using Decsion Tree with Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.922507Z",
     "start_time": "2019-06-17T07:22:49.736838Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature importance:  0.12633713537904698 0.3127400904012089 0.22076216233536947 0.34016061188437463\n",
      "training score:  0.9775533108866442\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#Create the target and features numpy arrays: target, features_one\n",
    "target = train['Survived'].values\n",
    "features_one = train[[\"Pclass\", \"Sex\", \"Age\", \"Fare\"]].values\n",
    "#Fit your first decision tree: my_tree_one\n",
    "my_tree_one = DecisionTreeClassifier()\n",
    "my_tree_one = my_tree_one.fit(features_one, target)\n",
    "#Look at the importance of the included features and print the score\n",
    "print('feature importance: ', *my_tree_one.feature_importances_)\n",
    "# Returns the mean accuracy on the given \"test\" data and labels.\n",
    "print('training score: ', my_tree_one.score(features_one, target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Why this is the wrong way!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.957984Z",
     "start_time": "2019-06-17T07:22:49.924694Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Impute the missing value with the median\n",
    "test.Fare[152] = test.Fare.median()\n",
    "test[\"Age\"] = test[\"Age\"].fillna(test[\"Age\"].median())\n",
    "#Convert the male and female groups to integer form\n",
    "test[\"Sex\"][test[\"Sex\"] == \"male\"] = 0\n",
    "test[\"Sex\"][test[\"Sex\"] == \"female\"] = 1\n",
    "\n",
    "#Impute the Embarked variable\n",
    "test[\"Embarked\"] = test[\"Embarked\"].fillna('S')\n",
    "#Convert the Embarked classes to integer form\n",
    "test[\"Embarked\"][test[\"Embarked\"] == \"S\"] = 0\n",
    "test[\"Embarked\"][test[\"Embarked\"] == \"C\"] = 1\n",
    "test[\"Embarked\"][test[\"Embarked\"] == \"Q\"] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.969368Z",
     "start_time": "2019-06-17T07:22:49.960252Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "test_features = test[[\"Pclass\", \"Sex\", \"Age\", \"Fare\"]].values\n",
    "# Make your prediction using the test set\n",
    "my_prediction = my_tree_one.predict(test_features)\n",
    "# Create a data frame with two columns: PassengerId & Survived. \n",
    "# Survived contains your predictions\n",
    "PassengerId =np.array(test['PassengerId']).astype(int)\n",
    "my_solution = pd.DataFrame(my_prediction, PassengerId, columns = [\"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.981347Z",
     "start_time": "2019-06-17T07:22:49.972283Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>893</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived\n",
       "892         0\n",
       "893         0\n",
       "894         1\n",
       "895         1\n",
       "896         1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_solution[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.987930Z",
     "start_time": "2019-06-17T07:22:49.983457Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that your data frame has 418 entries\n",
    "my_solution.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:49.996607Z",
     "start_time": "2019-06-17T07:22:49.990433Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Write your solution to a csv file with the name my_solution.csv \n",
    "my_solution.to_csv(\"./data/tatanic_solution_one.csv\", \n",
    "                   index_label = [\"PassengerId\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.012300Z",
     "start_time": "2019-06-17T07:22:49.999486Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9797979797979798\n"
     ]
    }
   ],
   "source": [
    "# Create a new array with the added features: features_two\n",
    "features_two = train[[\"Pclass\",\"Age\",\"Sex\",\"Fare\",\\\n",
    "                      \"SibSp\", \"Parch\", \"Embarked\"]].values\n",
    "\n",
    "my_tree_two = DecisionTreeClassifier()\n",
    "my_tree_two = my_tree_two.fit(features_two, target)\n",
    "\n",
    "#Print the score of the new decison tree\n",
    "print(my_tree_two.score(features_two, target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.024908Z",
     "start_time": "2019-06-17T07:22:50.014519Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9057239057239057\n"
     ]
    }
   ],
   "source": [
    "#Control overfitting by setting \"max_depth\" to 10 and \"min_samples_split\" to 5 : my_tree_two\n",
    "max_depth = 10\n",
    "min_samples_split = 5\n",
    "my_tree_two = DecisionTreeClassifier(max_depth = max_depth, \n",
    "                                          min_samples_split = min_samples_split, \n",
    "                                          random_state = 1)\n",
    "my_tree_two = my_tree_two.fit(features_two, target)\n",
    "\n",
    "#Print the score of the new decison tree\n",
    "print(my_tree_two.score(features_two, target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.043424Z",
     "start_time": "2019-06-17T07:22:50.026945Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9797979797979798\n"
     ]
    }
   ],
   "source": [
    "# create a new train set with the new variable\n",
    "train_two = train\n",
    "train_two['family_size'] = train.SibSp + train.Parch + 1\n",
    "\n",
    "# Create a new decision tree my_tree_three\n",
    "features_three = train[[\"Pclass\", \"Sex\", \"Age\", \\\n",
    "                        \"Fare\", \"SibSp\", \"Parch\", \"family_size\"]].values\n",
    "\n",
    "my_tree_three = DecisionTreeClassifier()\n",
    "my_tree_three = my_tree_three.fit(features_three, target)\n",
    "\n",
    "# Print the score of this decision tree\n",
    "print(my_tree_three.score(features_three, target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using Random Forest  with Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.094032Z",
     "start_time": "2019-06-17T07:22:50.045600Z"
    },
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.9640852974186308\n"
     ]
    }
   ],
   "source": [
    "#Import the `RandomForestClassifier`\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#We want the Pclass, Age, Sex, Fare,SibSp, Parch, and Embarked variables\n",
    "features_forest = train[[\"Pclass\", \"Age\", \"Sex\", \"Fare\", \"SibSp\", \"Parch\", \"Embarked\"]].values\n",
    "\n",
    "#Building the Forest: my_forest\n",
    "n_estimators = 100\n",
    "forest = RandomForestClassifier()\n",
    "my_forest = forest.fit(features_forest, target)\n",
    "\n",
    "#Print the score of the random forest\n",
    "print('Training score: ', my_forest.score(features_forest, target))\n",
    "\n",
    "#Compute predictions and print the length of the prediction vector:test_features, pred_forest\n",
    "test_features = test[[\"Pclass\", \"Age\", \"Sex\", \"Fare\", \"SibSp\", \"Parch\", \"Embarked\"]].values\n",
    "pred_forest = my_forest.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.267983Z",
     "start_time": "2019-06-17T07:22:50.096048Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9393939393939394\n"
     ]
    }
   ],
   "source": [
    "#Import the `RandomForestClassifier`\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#We want the Pclass, Age, Sex, Fare,SibSp, Parch, and Embarked variables\n",
    "features_forest = train[[\"Pclass\", \"Age\", \"Sex\", \"Fare\", \"SibSp\", \"Parch\", \"Embarked\"]].values\n",
    "\n",
    "#Building the Forest: my_forest\n",
    "n_estimators = 100\n",
    "forest = RandomForestClassifier(max_depth = 10, min_samples_split=2, \n",
    "                                n_estimators = n_estimators, random_state = 1)\n",
    "my_forest = forest.fit(features_forest, target)\n",
    "\n",
    "#Print the score of the random forest\n",
    "print(my_forest.score(features_forest, target))\n",
    "\n",
    "#Compute predictions and print the length of the prediction vector:test_features, pred_forest\n",
    "test_features = test[[\"Pclass\", \"Age\", \"Sex\", \"Fare\", \"SibSp\", \"Parch\", \"Embarked\"]].values\n",
    "pred_forest = my_forest.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-17T07:22:50.304850Z",
     "start_time": "2019-06-17T07:22:50.270951Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14130255 0.17906027 0.41616727 0.17938711 0.05039699 0.01923751\n",
      " 0.0144483 ]\n",
      "[0.10384741 0.20139027 0.31989322 0.24602858 0.05272693 0.04159232\n",
      " 0.03452128]\n",
      "0.9057239057239057\n",
      "0.9393939393939394\n"
     ]
    }
   ],
   "source": [
    "#Request and print the `.feature_importances_` attribute\n",
    "print(my_tree_two.feature_importances_)\n",
    "print(my_forest.feature_importances_)\n",
    "\n",
    "#Compute and print the mean accuracy score for both models\n",
    "print(my_tree_two.score(features_two, target))\n",
    "print(my_forest.score(features_two, target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 作业\n",
    "https://www.datacamp.com/community/tutorials/the-importance-of-preprocessing-in-data-science-and-the-machine-learning-pipeline-i-centering-scaling-and-k-nearest-neighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
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    }
   },
   "source": [
    "# 阅读材料\n",
    "机器学习算法的要点（附 Python 和 R 代码）http://blog.csdn.net/a6225301/article/details/50479672\n",
    "\n",
    "The \"Python Machine Learning\" book code repository and info resource https://github.com/rasbt/python-machine-learning-book\n",
    "\n",
    "An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013) : Python code https://github.com/JWarmenhoven/ISLR-python\n",
    "\n",
    "BuildingMachineLearningSystemsWithPython https://github.com/luispedro/BuildingMachineLearningSystemsWithPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Contents](Index.ipynb) | [Introducing Scikit-Learn](09.02-machine-learning-with-sklearn.ipynb) >"
   ]
  }
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